September 10, 2024 (Update: September 14, 2024 ) 0 Comments
This note is for Lu, Y., & Zhou, H. H. (2016). Statistical and Computational Guarantees of Lloyd’s Algorithm and its Variants (No. arXiv:1612.02099). arXiv. http://arxiv.org/abs/1612.02099
Continue reading
September 10, 2024 (Update: September 14, 2024 ) 0 Comments
This note is for Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., Taychameekiatchai, A., Rao, P., Pisco, A. O., Zou, J., Wang, B., Gandal, M., Raychaudhuri, S., Pasaniuc, B., & Price, A. L. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(10), 1572–1580. https://doi.org/10.1038/s41588-022-01167-z
Continue reading
August 05, 2024 (Update: August 21, 2024 ) 0 Comments
This note is for the discussion paper Leiner, J., Duan, B., Wasserman, L., & Ramdas, A. (2023). Data fission: Splitting a single data point (arXiv:2112.11079). arXiv. http://arxiv.org/abs/2112.11079 in the JASA invited session at JSM 2024
Continue reading
July 30, 2024 (Update: August 16, 2024 ) 0 Comments
This note is for Wainwright, M. J. (n.d.). High-Dimensional Statistics: A Non-Asymptotic Viewpoint. 604.
Continue reading
July 04, 2024 (Update: August 16, 2024 ) 0 Comments
This is the note for Martin, G. M., Frazier, D. T., & Robert, C. P. (2024). Approximating Bayes in the 21st Century. Statistical Science, 39(1), 20–45. https://doi.org/10.1214/22-STS875
Continue reading
August 05, 2024 (Update: August 16, 2024 )
This is the note for the talk Statistical Inference in Large Language Models: Alignment and Copyright given by Weijie Su at JSM 2024
Continue reading
August 05, 2024 (Update: August 16, 2024 )
This is the note for the talk LLMs training given by Linjun Zhang at JSM 2024
Continue reading
August 05, 2024 (Update: August 16, 2024 ) 0 Comments
This is the note for the talk Statistical Inference in Large Language Models: A Statistical Framework of Watermarks given by Weijie Su at JSM 2024
Continue reading
February 20, 2024 (Update: May 23, 2024 )
This post is for Lin, Z., Kong, D., & Wang, L. (2023). Causal inference on distribution functions. Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(2), 378–398.
Continue reading
April 20, 2024 (Update: May 08, 2024 )
This note is for Candes, E., Fan, Y., Janson, L., & Lv, J. (2017). Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection. arXiv:1610.02351 [Math, Stat].
Continue reading
January 10, 2024 (Update: March 28, 2024 )
This post is based on vignettes of MMRM R package: https://openpharma.github.io/mmrm/main/index.html
Continue reading
February 24, 2024 (Update: March 07, 2024 )
The note is for Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20(1), 296.
Continue reading
January 26, 2024 (Update: February 06, 2024 )
This note is for Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021). Effective gene expression prediction from sequence by integrating long-range interactions. Nature Methods, 18(10), 1196–1203.
Continue reading
January 31, 2024 (Update: February 06, 2024 )
This post is for Köhler, M., Umlauf, N., Beyerlein, A., Winkler, C., Ziegler, A.-G., & Greven, S. (2017). Flexible Bayesian additive joint models with an application to type 1 diabetes research. Biometrical Journal, 59(6), 1144–1165.
Continue reading
January 31, 2024 (Update: February 06, 2024 )
This post is for Liu, M., Sun, J., Herazo-Maya, J. D., Kaminski, N., & Zhao, H. (2019). Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension. Statistics in Biosciences, 11(3), 614–629.
Continue reading
January 15, 2024 (Update: January 15, 2024 )
This post is for Dai, C., Lin, B., Xing, X., & Liu, J. S. (2023). A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models. Journal of the American Statistical Association, 118(543), 1551–1565.
Continue reading
January 06, 2024 (Update: January 13, 2024 )
This post is for Chen, Shuxiao, Sizun Jiang, Zongming Ma, Garry P. Nolan, and Bokai Zhu. “One-Way Matching of Datasets with Low Rank Signals.” arXiv, October 3, 2022.
Continue reading
December 19, 2020 (Update: January 13, 2024 )
This note is for Dai, C., Lin, B., Xing, X., & Liu, J. S. (2020). False Discovery Rate Control via Data Splitting. ArXiv:2002.08542 [Stat].
Continue reading
December 08, 2023 (Update: December 30, 2023 )
The post is for Neufeld, Anna, Lucy L Gao, Joshua Popp, Alexis Battle, and Daniela Witten. “Inference after Latent Variable Estimation for Single-cell RNA Sequencing Data.” Biostatistics, December 13, 2022, kxac047.
Continue reading
September 14, 2023 (Update: December 04, 2023 )
This note is for Hou, W., Ji, Z., Chen, Z., Wherry, E. J., Hicks, S. C., & Ji, H. (2021). A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples (p. 2021.07.10.451910). bioRxiv.
Continue reading
November 01, 2019 (Update: November 16, 2023 )
The post is based on Zhou, H., Hu, L., Zhou, J., & Lange, K. (2019). MM Algorithms for Variance Components Models. Journal of Computational and Graphical Statistics, 28(2), 350–361.
Continue reading
June 11, 2023 (Update: July 27, 2023 )
The note is for Das, K., Li, J., Wang, Z., Tong, C., Fu, G., Li, Y., Xu, M., Ahn, K., Mauger, D., Li, R., & Wu, R. (2011). A dynamic model for genome-wide association studies. Human Genetics, 129(6), 629–639.
Continue reading
June 11, 2023 (Update: July 27, 2023 )
This post is for Marchetti-Bowick, M., Yin, J., Howrylak, J. A., & Xing, E. P. (2016). A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits. Bioinformatics, 32(19), 2903–2910.
Continue reading
July 14, 2023 (Update: July 27, 2023 )
This note is for Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020). Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods, 17(2), Article 2.
Continue reading
June 11, 2023 (Update: June 28, 2023 )
This post is for Ko, S., German, C. A., Jensen, A., Shen, J., Wang, A., Mehrotra, D. V., Sun, Y. V., Sinsheimer, J. S., Zhou, H., & Zhou, J. J. (2022). GWAS of longitudinal trajectories at biobank scale. The American Journal of Human Genetics, 109(3), 433–445.
Continue reading
January 12, 2022 (Update: April 22, 2023 )
Continue reading
December 15, 2022 (Update: February 07, 2023 )
This note is for Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3), 291–295.
Continue reading
August 17, 2017 (Update: January 13, 2023 ) 0 Comments
Survival analysis examines and models the time it takes for events to occur. It focuses on the distribution of survival times. There are many well known methods for estimating unconditional survival distribution, and they examines the relationship between survival and one or more predictors, usually terms covariates in the survival-analysis literature. And Cox Proportional-Hazards regression model is one of the most widely used method of survival analysis.
Continue reading
December 28, 2022 (Update: December 31, 2022 )
This post is for Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014). Similarity network fusion for aggregating data types on a genomic scale. Nature Methods, 11(3), Article 3. and a related paper Ruan, P., Wang, Y., Shen, R., & Wang, S. (2019). Using association signal annotations to boost similarity network fusion. Bioinformatics, 35(19), 3718–3726.
Continue reading
July 18, 2019 (Update: December 19, 2022 )
This note is based on
Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. (2011). Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test. American Journal of Human Genetics, 89(1), 82–93.
Wang, M. H., Weng, H., Sun, R., Lee, J., Wu, W. K. K., Chong, K. C., & Zee, B. C.-Y. (2017). A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests. Bioinformatics, 33(15), 2330–2336.
Continue reading
May 06, 2019 (Update: December 14, 2022 )
This note is based on Chapter 1 of Lehmann EL, Romano JP. Testing statistical hypotheses. Springer Science & Business Media; 2006 Mar 30.
Continue reading
July 15, 2022 (Update: November 17, 2022 )
This note is based on Cai, Z., Poulos, R. C., Liu, J., & Zhong, Q. (2022). Machine learning for multi-omics data integration in cancer. IScience, 25(2), 103798.
Continue reading
November 04, 2022 (Update: November 14, 2022 )
This note is for Blondel, M., Teboul, O., Berthet, Q., & Djolonga, J. (2020). Fast Differentiable Sorting and Ranking (arXiv:2002.08871). arXiv.
Continue reading
November 12, 2022 (Update: November 14, 2022 )
This note is for Jiang, W., & Yu, W. (2017). Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies. Bioinformatics, 33(4), 500–507.
Continue reading
September 22, 2021 (Update: October 12, 2022 )
This post is based on the first version of Shin, M., Wang, L., & Liu, J. S. (2020). Scalable Uncertainty Quantification via GenerativeBootstrap Sampler. , which is lately updated as Shin, M., Wang, S., & Liu, J. S. (2022). Generative Multiple-purpose Sampler for Weighted M-estimation (arXiv:2006.00767; Version 2). arXiv.
Continue reading
July 15, 2019 (Update: October 09, 2022 )
This note is for Cai, T. T., & Zhang, L. (2019). High dimensional linear discriminant analysis: Optimality, adaptive algorithm and missing data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(4), 675–705.
Continue reading
October 02, 2022 (Update: October 02, 2022 )
This post is based on Rizopoulos, D. (2017). An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R. 235.
Continue reading
September 22, 2021 (Update: September 22, 2022 ) 0 Comments
The note is based on Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018). Distribution-Free Predictive Inference for Regression. Journal of the American Statistical Association, 113(523), 1094–1111. and Tibshirani, R. J., Candès, E. J., Barber, R. F., & Ramdas, A. (2019). Conformal Prediction Under Covariate Shift. Proceedings of the 33rd International Conference on Neural Information Processing Systems, 2530–2540.
Continue reading
August 25, 2022 (Update: August 31, 2022 )
This note is for Todd, J. L., Vinisko, R., Liu, Y., Neely, M. L., Overton, R., Flaherty, K. R., Noth, I., Newby, L. K., Lasky, J. A., Olman, M. A., Hesslinger, C., Leonard, T. B., Palmer, S. M., & Belperio, J. A. (2020). Circulating matrix metalloproteinases and tissue metalloproteinase inhibitors in patients with idiopathic pulmonary fibrosis in the multicenter IPF-PRO Registry cohort. BMC Pulmonary Medicine, 20(1), 64.
Continue reading
November 05, 2020 (Update: June 14, 2022 )
This note is for Fitzgibbon, A. W. (2003). Robust registration of 2D and 3D point sets. Image and Vision Computing, 21(13), 1145–1153.
Continue reading
May 11, 2022 (Update: June 14, 2022 )
This note is for Hall, P., & Heckman, N. E. (2000). Testing for Monotonicity of a Regression Mean by Calibrating for Linear Functions. The Annals of Statistics, 28(1), 20–39.
Continue reading
March 22, 2022 (Update: March 25, 2022 )
This note is for Pitman, E. J. G. (1939). The Estimation of the Location and Scale Parameters of a Continuous Population of any Given Form. Biometrika, 30(3/4), 391–421. and Kagan, AM & Rukhin, AL. (1967). On the estimation of a scale parameter. Theory of Probability \& Its Applications, 12, 672–678.
Continue reading
September 16, 2021 (Update: March 18, 2022 ) 0 Comments
This note collects several references on the research of cross-validation.
Continue reading
January 08, 2020 (Update: January 10, 2022 )
This note is for Section 5.3 of Breiman, L. (Ed.). (1998). Classification and regression trees (1. CRC Press repr). Chapman & Hall/CRC.
Continue reading
May 26, 2021 (Update: January 08, 2022 ) 0 Comments
This note is for Firtina, C., Bar-Joseph, Z., Alkan, C., & Cicek, A. E. (2018). Hercules: A profile HMM-based hybrid error correction algorithm for long reads. Nucleic Acids Research, 46(21), e125.
Continue reading
November 18, 2021 (Update: December 07, 2021 ) 0 Comments
This note is based on Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., & Ueda, N. (n.d.). Learning Systems of Concepts with an Infinite Relational Model. 8. and Saad, F. A., & Mansinghka, V. K. (2021). Hierarchical Infinite Relational Model. ArXiv:2108.07208 [Cs, Stat].
Continue reading
June 24, 2019 (Update: November 30, 2021 )
This post is based on Hastie, T., Montanari, A., Rosset, S., & Tibshirani, R. J. (2019). Surprises in High-Dimensional Ridgeless Least Squares Interpolation. 53.
Continue reading
July 05, 2021 (Update: July 06, 2021 ) 0 Comments
This note is for Xing, J., Ai, H., & Lao, S. (2009). Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 1200–1207.
Continue reading
April 25, 2021 (Update: May 24, 2021 ) 0 Comments
This note is for Payer, C., Štern, D., Neff, T., Bischof, H., & Urschler, M. (2018). Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks. ArXiv:1806.02070 [Cs].
Continue reading
March 06, 2019 (Update: May 14, 2021 ) 0 Comments
This post reviewed the topic of path sampling in the lecture slides of STAT 5020 , and noted a general path sampling described by Gelman and Meng (1998) , then used a toy example to illustrate it with Stan programming language.
Continue reading
March 03, 2019 (Update: April 12, 2021 ) 0 Comments
This report is motivated by comments under Larry’s post, Modern Two-Sample Tests .
Continue reading
March 09, 2021 (Update: March 12, 2021 ) 0 Comments
This note is based on He, X., & Shi, P. (1998). Monotone B-Spline Smoothing. Journal of the American Statistical Association, 93(442), 643–650. , and the reproduced simulations are based on the updated algorithm, Ng, P., & Maechler, M. (2007). A fast and efficient implementation of qualitatively constrained quantile smoothing splines. Statistical Modelling, 7(4), 315–328.
Continue reading
September 28, 2020 (Update: January 21, 2021 )
This post is mainly based on Hastie, T., & Stuetzle, W. (1989). Principal Curves. Journal of the American Statistical Association.
Continue reading
December 31, 2018 (Update: December 24, 2020 )
The note is for Wagner, A. (2012). Metabolic Networks and Their Evolution. In O. S. Soyer (Ed.), Evolutionary Systems Biology (Vol. 751, pp. 29–52). Springer New York.
Continue reading
November 12, 2020 (Update: November 13, 2020 )
This note is for Huang, M., Shah, N. D., & Yao, L. (2019). Evaluating global and local sequence alignment methods for comparing patient medical records. BMC Medical Informatics and Decision Making, 19(6), 263.
Continue reading
November 07, 2020 (Update: November 08, 2020 )
This note is for Rusinkiewicz, S., & Levoy, M. (2001). Efficient variants of the ICP algorithm. Proceedings Third International Conference on 3-D Digital Imaging and Modeling, 145–152. .
Continue reading
September 24, 2020 (Update: September 28, 2020 )
This post is based on Jaqaman, K., Loerke, D., Mettlen, M., Kuwata, H., Grinstein, S., Schmid, S. L., & Danuser, G. (2008). Robust single-particle tracking in live-cell time-lapse sequences. Nature Methods, 5(8), 695–702.
Continue reading
June 08, 2020 (Update: June 09, 2020 )
This note is for Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., Pinello, L., Skums, P., Stamatakis, A., Attolini, C. S.-O., Aparicio, S., Baaijens, J., Balvert, M., Barbanson, B. de, Cappuccio, A., … Schönhuth, A. (2020). Eleven grand challenges in single-cell data science. Genome Biology, 21(1), 31.
Continue reading
April 26, 2020 (Update: April 30, 2020 )
This post is based on Coffey, N., Harrison, A. J., Donoghue, O. A., & Hayes, K. (2011). Common functional principal components analysis: A new approach to analyzing human movement data. Human Movement Science, 30(6), 1144–1166.
Continue reading
January 07, 2019 (Update: April 21, 2020 ) 0 Comments
In this note, the material about Jackknife is based on Wasserman (2006) and Efron and Hastie (2016) , while the Jackknife estimation of Mutual Information is based on Zeng et al. (2018) .
Continue reading
February 29, 2020 (Update: March 29, 2020 )
This post is based on Benko, M., Härdle, W., & Kneip, A. (2009). Common functional principal components. The Annals of Statistics, 37(1), 1–34.
Continue reading
February 22, 2020 (Update: March 16, 2020 )
kjytay’s blog summarizes some properties of equicorrelation matix, which has the following form,
Continue reading
September 18, 2019 (Update: March 01, 2020 )
This note is based on Ma, J., Du, K., & Gu, G. (2019). An efficient exponential twisting importance sampling technique for pricing financial derivatives. Communications in Statistics - Theory and Methods, 48(2), 203–219.
Continue reading
January 17, 2020 (Update: February 15, 2020 )
This post is based on the talk given by Dr. Yue Wang at the Department of Statistics and Data Science, Southern University of Science and Technology on Jan. 04, 2020.
Continue reading
February 10, 2020 (Update: February 15, 2020 )
This post is based on the talk given by T. Kanamori at the 11th ICSA International Conference on Dec. 22nd, 2019.
Continue reading
March 11, 2019 (Update: January 31, 2020 )
Prof. Inchi HU will give a talk on Large Scale Inference for Chi-squared Data tomorrow, which proposes the Tweedie’s formula in the Bayesian hierarchical model for chi-squared data, and he mentioned a thought-provoking paper, Efron, B. (2011). Tweedie’s Formula and Selection Bias. Journal of the American Statistical Association, 106(496), 1602–1614. , which is the focus of this note.
Continue reading
January 05, 2020 (Update: January 30, 2020 )
This post is based on the talk, Gradient-based Sparse Principal Component Analysis, given by Dr. Yixuan Qiu at the Department of Statistics and Data Science, Southern University of Science and Technology on Jan. 05, 2020.
Continue reading
December 21, 2019 (Update: January 30, 2020 )
This post is based on the Pao-Lu Hsu Award Lecture given by Prof. Hongyu Zhao at the 11th ICSA International Conference on Dec. 21th, 2019.
Continue reading
January 21, 2020 (Update: January 29, 2020 )
This post is based on the seminar, Data Acquisition, Registration and Modelling for Multi-dimensional Functional Data , given by Prof. Shi .
Continue reading
January 16, 2020 (Update: January 17, 2020 )
This post is based on the material of the second lecture of STAT 6050 instructed by Prof. Wicker , and mainly refer some more formally description from the book, Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar - Foundations of Machine Learning-The MIT Press (2012) .
Continue reading
December 20, 2019 (Update: January 16, 2020 )
This post is based on the Peter Hall Lecture given by Prof. Jianqing Fan at the 11th ICSA International Conference on Dec. 20th, 2019.
Continue reading
March 26, 2019 (Update: January 16, 2020 )
Prof. Jon A. WELLNER introduced the application of a new multiplier inequality on lasso in the distinguish lecture , which reminds me that it is necessary to read more theoretical results of lasso, and so this is the post, which is based on Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical Learning with Sparsity. 362.
Continue reading
January 11, 2020 (Update: January 15, 2020 )
This post is based on the talk, Next-Generation Statistical Methods for Association Analysis of Now-Generation Sequencing Studies, given by Dr. Xiang Zhan at the Department of Statistics and Data Science, Southern University of Science and Technology on Jan. 05, 2020.
Continue reading
December 17, 2019 (Update: January 02, 2020 )
This post is based on the slides for the talk given by Zijian Guo at The International Statistical Conference In Memory of Professor Sik-Yum Lee
Continue reading
December 10, 2018 (Update: December 25, 2019 )
This post is the online version of my report for the Project 2 of STAT 5050 taught by Prof. Wei .
Continue reading
August 07, 2018 (Update: December 05, 2019 )
This post is the notes for Mithani et al. (2009) .
Continue reading
December 04, 2019 (Update: December 04, 2019 )
The post is based on the BIOS Consortium, van Iterson, M., van Zwet, E. W., & Heijmans, B. T. (2017). Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution. Genome Biology, 18(1), 19.
Continue reading
November 04, 2019 (Update: December 03, 2019 )
This note is based on Huang, Y.-T. (2019). Variance component tests of multivariate mediation effects under composite null hypotheses. Biometrics, 0(0).
Continue reading
December 02, 2019 (Update: December 03, 2019 )
This post is based on section 8.3 of Casella and Berger (2001).
Continue reading
October 29, 2019 (Update: November 27, 2019 )
This post is based on Li, Y., Wang, N., & Carroll, R. J. (2010). Generalized Functional Linear Models With Semiparametric Single-Index Interactions. Journal of the American Statistical Association, 105(490), 621–633.
Continue reading
November 19, 2019 (Update: November 25, 2019 )
This post is based on Li, H., & Zhou, Q. (2019). Gaussian DAGs on network data. ArXiv:1905.10848 [Cs, Stat].
Continue reading
August 26, 2019 (Update: November 03, 2019 )
This post is based on Fan, J., Weng, H., & Zhou, Y. (2019). Optimal estimation of functionals of high-dimensional mean and covariance matrix. ArXiv:1908.07460 [Math, Stat].
Continue reading
January 05, 2019 (Update: November 01, 2019 ) 0 Comments
Continue reading
January 08, 2019 (Update: November 01, 2019 )
This note is based on Li (1991) and Ma and Zhu (2012) .
Continue reading
September 17, 2019 (Update: October 10, 2019 )
This note is based on Shao, J., Wang, Y., Deng, X., & Wang, S. (2011). Sparse linear discriminant analysis by thresholding for high dimensional data. The Annals of Statistics, 39(2), 1241–1265.
Continue reading
September 17, 2019 (Update: September 20, 2019 ) 0 Comments
This note is based on Fan, J., & Fan, Y. (2008). High-dimensional classification using features annealed independence rules. The Annals of Statistics, 36(6), 2605–2637.
Continue reading
August 16, 2019 (Update: September 13, 2019 )
This post is based on Candes, E., & Tao, T. (2007). The Dantzig selector: Statistical estimation when $p$ is much larger than $n$. The Annals of Statistics, 35(6), 2313–2351.
Continue reading
July 05, 2019 (Update: July 25, 2019 )
This post is based on Lauritzen, S., Uhler, C., & Zwiernik, P. (2019). Maximum likelihood estimation in Gaussian models under total positivity. The Annals of Statistics, 47(4), 1835–1863.
Continue reading
July 08, 2019 (Update: July 13, 2019 )
This note is based on Zhou, T., Sengupta, S., Müller, P., & Ji, Y. (2019). TreeClone: Reconstruction of tumor subclone phylogeny based on mutation pairs using next generation sequencing data. The Annals of Applied Statistics, 13(2), 874–899.
Continue reading
June 28, 2019 (Update: July 12, 2019 )
This note is based on Chapter 15 of Wainwright, M. (2019). High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge: Cambridge University Press.
Continue reading
May 28, 2019 (Update: June 23, 2019 )
Continue reading
May 07, 2019 (Update: June 23, 2019 )
Continue reading
May 09, 2019 (Update: June 23, 2019 )
Continue reading
January 18, 2019 (Update: April 09, 2019 ) 0 Comments
This note is for Doucet, A., & Johansen, A. M. (2009). A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of Nonlinear Filtering, 12(656–704), 3. For the sake of clarity, I split the general SMC methods (section 3) into my next post.
Continue reading
March 20, 2019 (Update: April 08, 2019 )
I read the topic in kiytay’s blog: Proximal operators and generalized gradient descent , and then read its reference, Hastie et al. (2015) , and write some program to get a better understanding.
Continue reading
March 26, 2019 (Update: March 28, 2019 )
This note is for Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Zhao, X., & Kim, T.-K. (2014). Multiple Object Tracking: A Literature Review. ArXiv:1409.7618 [Cs].
Continue reading
June 04, 2017 (Update: March 12, 2019 ) 0 Comments
Gibbs sampler is an iterative algorithm that constructs a dependent sequence of parameter values whose distribution converges to the target joint posterior distribution.
Continue reading
March 07, 2019 (Update: March 12, 2019 )
Prof. YUAN Ming will give a distinguish lecture on Low Rank Tensor Methods in High Dimensional Data Analysis . To get familiar with his work on tensor, I read his paper, Yuan, M., & Zhang, C.-H. (2016). On Tensor Completion via Nuclear Norm Minimization. Foundations of Computational Mathematics, 16(4), 1031–1068. , which is the topic of this post.
Continue reading
February 23, 2019 (Update: March 09, 2019 )
This note is based on Wong, S. W. K., Liu, J. S., & Kou, S. C. (2018). Exploring the conformational space for protein folding with sequential Monte Carlo. The Annals of Applied Statistics, 12(3), 1628–1654.
Continue reading
March 04, 2019 (Update: March 05, 2019 )
Larry wrote that “Noninformative priors are a lost cause” in his post, LOST CAUSES IN STATISTICS II: Noninformative Priors , and he mentioned his review paper Kass and Wasserman (1996) on noninformative priors. This note is for this paper.
Continue reading
February 25, 2019 (Update: February 27, 2019 )
This note is based on Loskot, P., Atitey, K., & Mihaylova, L. (2019). Comprehensive review of models and methods for inferences in bio-chemical reaction networks.
Continue reading
July 16, 2017 (Update: January 31, 2019 ) 0 Comments
This report shows how to use importance sampling to estimate the expectation.
Continue reading
June 10, 2017 (Update: January 31, 2019 ) 0 Comments
The first peep to SMC as an abecedarian, a more comprehensive note can be found here .
Continue reading
September 08, 2017 (Update: January 30, 2019 ) 0 Comments
There is an important probability distribution used in many applications, the chain-structured model.
Continue reading
September 07, 2017 (Update: January 30, 2019 ) 0 Comments
Continue reading
July 17, 2017 (Update: January 30, 2019 ) 0 Comments
This report implements the simulation of growing a polymer under the self-avoid walk model, and summary the sequential importance sampling techniques for this problem.
Continue reading
There are my notes when I read the paper called Genetic network inference .
Continue reading
There are my notes when I read the paper called System Genetic Approach .
Continue reading
There are my notes when I read the paper called Maximal information component analysis .
Continue reading
There are my notes when I read the paper called Detecting Novel Associations
in Large Data Sets .
Continue reading
This is the implement in R of MINE.
Continue reading
Continue reading
Use the e1071 library in R to demonstrate the support vector classifier and the SVM.
Continue reading
Continue reading
Continue reading
This note is for Moral, P. D., Doucet, A., & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(3), 411–436.
Continue reading
June 11, 2017
Continue reading
Any time series without a constant mean over time is nonstationary.
Continue reading
Continue reading
For a given time series, how to choose appropriate values for $p, d, q$
Continue reading
Continue reading
Monte Carlo plays a key role in evaluating integrals and simulating stochastic systems, and the most critical step of Monte Carlo algorithm is sampling from an appropriate probability distribution $\pi (\mathbf x)$. There are two ways to solve this problem, one is to do importance sampling , another is to produce statistically dependent samples based on the idea of Markov chain Monte Carlo sampling .
Continue reading
Continue reading
Continue reading
Continue reading
“The p value was never meant to be used the way it’s used today.” –Goodman
Continue reading
The conjugate gradient method is an iterative method for solving a linear system of equations, so we can use conjugate method to estimate the parameters in (linear/ridge) regression.
Continue reading
Continue reading
This post is the notes of this paper .
Continue reading
This post is for The Human Microbiome Project Consortium, Huttenhower, C., Gevers, D., Knight, R., Abubucker, S., Badger, J. H., … White, O. (2012). Structure, function and diversity of the healthy human microbiome. Nature, 486(7402), 207–214.
Continue reading
The note is for Kirschner, D. E., & Blaser, M. J. (1995). The dynamics of helicobacter pylori infection of the human stomach. Journal of Theoretical Biology, 176(2), 281–290.
Continue reading
Continue reading
Sebastian Schreiber gave a talk titled Persistence of species in the face of environmental stochasticity .
Continue reading
Repeated Linear Regression means that repeat the fitting of linear regression for many times, and there are some common parts among these regressions.
Continue reading
Repeated Linear Regressions refer to a set of linear regressions in which there are several same variables.
Continue reading
Discuss three different methods for formulating stochastic epidemic models.
Continue reading
This post aims to clarify the relationship between rates and probabilities.
Continue reading
Continue reading
Continue reading
August 18, 2018
The note is for Dietterich, T. and Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research 2: 263–286. .
Continue reading
August 24, 2018
The note is for Gilks, W. R., Richardson, S., & Spiegelhalter, D. (Eds.). (1995). Markov chain Monte Carlo in practice. CRC press. .
Continue reading
December 30, 2018
The note is for Chapter 1 of Soyer, Orkun S., ed. 2012 Evolutionary Systems Biology. Advances in Experimental Medicine and Biology, 751. New York: Springer .
Continue reading
January 02, 2019
The note is for Wagner, A., & Fell, D. A. (2001). The small world inside large metabolic networks. Proceedings of the Royal Society of London B: Biological Sciences, 268(1478), 1803-1810. .
Continue reading
January 06, 2019
The note is for Sun, Q., Zhu, R., Wang, T., & Zeng, D. (2017). Counting Process Based Dimension Reduction Methods for Censored Outcomes. ArXiv:1704.05046 [Stat].
Continue reading
January 09, 2019
This note is based on Yuan, Y., Shen, X., Pan, W., & Wang, Z. (2019). Constrained likelihood for reconstructing a directed acyclic Gaussian graph. Biometrika, 106(1), 109–125.
Continue reading
January 10, 2019
The note is for Green, P.J. (1995). “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination”. Biometrika. 82 (4): 711–732.
Continue reading
January 13, 2019
This note is for Fan, Z., & Guan, L. (2018). Approximate $\ell_{0}$-penalized estimation of piecewise-constant signals on graphs. The Annals of Statistics, 46(6B), 3217–3245.
Continue reading
January 15, 2019
This note is based on Cook, R. D., & Forzani, L. (2019). Partial least squares prediction in high-dimensional regression. The Annals of Statistics, 47(2), 884–908.
Continue reading
January 19, 2019
This note is for Section 3 of Doucet, A., & Johansen, A. M. (2009). A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of Nonlinear Filtering, 12(656–704), 3. , and it is the complement of my previous post.
Continue reading
January 21, 2019
Continue reading
January 24, 2019
This note is for Wang, L., Wang, S., & Bouchard-Côté, A. (2018). An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics. ArXiv:1806.08813 [q-Bio, Stat].
Continue reading
January 28, 2019
This is the note for Neal, R. M. (1998). Annealed Importance Sampling. ArXiv:Physics/9803008.
Continue reading
January 30, 2019
The note is for Fourment, M., Magee, A. F., Whidden, C., Bilge, A., Matsen IV, F. A., & Minin, V. N. (2018). 19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology.
Continue reading
February 12, 2019
This post caught a glimpse of the pseudolikelihood.
Continue reading
February 12, 2019
The note is for Nelder, J. A., & Lee, Y. (1992). Likelihood, Quasi-Likelihood and Pseudolikelihood: Some Comparisons. Journal of the Royal Statistical Society. Series B (Methodological), 54(1), 273–284.
Continue reading
February 13, 2019
This note is based on Fan, X., Pyne, S., & Liu, J. S. (2010). Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle. The Annals of Applied Statistics, 4(2), 988–1013.
Continue reading
February 13, 2019
This note is based on Chapter 7 of Hoff PD. A first course in Bayesian statistical methods. Springer Science & Business Media; 2009 Jun 2.
Continue reading
February 13, 2019
This note is based on Varin, C., Reid, N., & Firth, D. (2011). AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS. Statistica Sinica, 21(1), 5–42. , a survey of recent developments in the theory and application of composite likelihood.
Continue reading
In Prof. Shao ’s wonderful talk, Wandering around the Asymptotic Theory , he mentioned the Studentized U-statistics. I am interested in the derivation of the variances in the denominator.
Continue reading
February 16, 2019
This note is based on LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Continue reading
February 17, 2019
This note is for Polson, N. G., & Sokolov, V. (2017). Deep Learning: A Bayesian Perspective. Bayesian Analysis, 12(4), 1275–1304.
Continue reading
February 18, 2019
The paper, Greenshtein and Ritov (2004) , is recommended by
Larry Wasserman in his post Consistency, Sparsistency and Presistency .
Continue reading
February 19, 2019
I encounter the term RIP in Larry Wasserman ’s post, RIP RIP (Restricted Isometry Property, Rest In Peace) , and also find some material in Hastie et al.’s book: Statistical Learning with Sparsity about RIP.
Continue reading
February 20, 2019
This note is based on Karl Sigman ’s IEOR 6711: Continuous-Time Markov Chains .
Continue reading
February 21, 2019
I learned Stein’s Paradox from Larry Wasserman ’s post, STEIN’S PARADOX , perhaps I had encountered this term before but I cannot recall anything about it. (I am guilty)
Continue reading
March 07, 2019
A brief summary of the post, Eid ma clack shaw zupoven del ba .
Continue reading
March 08, 2019
I noticed that the papers of matrix/tensor completion always talk about the Bernstein inequality, then I picked the Bernstein Bounds discussed in Wainwright (2019) .
Continue reading
March 12, 2019
This note is for Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of Science. The Annals of Applied Statistics, 1(1), 17–35.
Continue reading
March 13, 2019
This note is for Volgushev, S., Chao, S.-K., & Cheng, G. (2019). Distributed inference for quantile regression processes. The Annals of Statistics, 47(3), 1634–1662.
Continue reading
March 14, 2019
Continue reading
March 15, 2019
Continue reading
March 19, 2019
Continue reading
March 25, 2019
This note is for Han, Q., & Wellner, J. A. (2017). Convergence rates of least squares regression estimators with heavy-tailed errors.
Continue reading
March 25, 2019
I happened to read Yixuan’s blog about a question related to the course Statistical Inference , whether two marginal distributions can determine the joint distribution. The question is adopted from Exercise 4.47 of Casella and Berger (2002) .
Continue reading
March 29, 2019
This note is for Fan, J., Ke, Y., Sun, Q., & Zhou, W.-X. (2017). FarmTest: Factor-Adjusted Robust Multiple Testing with Approximate False Discovery Control. ArXiv:1711.05386 [Stat]. .
Continue reading
March 31, 2019
This note is for Efron’s slide: Frequentist Accuracy of Bayesian Estimates , which is recommended by Larry’s post: Shaking the Bayesian Machine .
Continue reading
April 01, 2019
This post is based on Chapter 7 of Statistical Learning with Sparsity: The Lasso and Generalizations , and I wrote R program to reproduce the simulations to get a better understanding.
Continue reading
April 02, 2019
This note is for Padfield, D., Rittscher, J., & Roysam, B. (2011). Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Medical Image Analysis, 15(4), 650–668. .
Continue reading
April 04, 2019
This note is based on Larry’s post, Mixture Models: The Twilight Zone of Statistics .
Continue reading
April 08, 2019
This post is mainly based on Hastie et al. (2015) , and incorporated with some materials from Watson (1992) .
Continue reading
April 09, 2019
This note is for Khan, Z., Balch, T., & Dellaert, F. (2004). An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets. In T. Pajdla & J. Matas (Eds.), Computer Vision - ECCV 2004 (pp. 279–290). Springer Berlin Heidelberg.
Continue reading
April 09, 2019
This post is for the survey paper, Meijering, E., Dzyubachyk, O., & Smal, I. (2012). Chapter nine - Methods for Cell and Particle Tracking. In P. M. conn (Ed.), Methods in Enzymology (pp. 183–200).
Continue reading
April 10, 2019
Larry discussed the normalizing constant paradox in his blog .
Continue reading
April 10, 2019
This note is for Smal, I., Meijering, E., Draegestein, K., Galjart, N., Grigoriev, I., Akhmanova, A., … Niessen, W. (2008). Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering. Medical Image Analysis, 12(6), 764–777.
Continue reading
April 15, 2019
This note is based on the Chapter 6 of Hastie, T., Tibshirani, R., & Wainwright, M. (2015). Statistical Learning with Sparsity. 362. .
Continue reading
April 15, 2019
In the last lecture of STAT 5030 , Prof. Lin shared one of the results in the paper, Neykov, M., Liu, J. S., & Cai, T. (2016). L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs. Journal of Machine Learning Research, 17(87), 1–37. , or say the start point for the paper—the following Lemma. Because it seems that the condition and the conclusion is completely same with Sliced Inverse Regression, except for a direct interpretation—the least square regression.
Continue reading
April 20, 2019
Materials from STAT 5030 .
Continue reading
May 01, 2019
This note consists of the lecture material of STAT 6060 taught by Prof. Shao , four homework (indexed by “Homework”) and several personal comments (indexed by “Note”).
Continue reading
June 23, 2019
Last two days, I attended the conference Medicine Meets AI 2019: East Meets West , which help me know more AI from the industrial and medical perspective.
Continue reading
June 27, 2019
This note is for Cockayne, J., Oates, C. J., Ipsen, I. C. F., & Girolami, M. (2018). A Bayesian Conjugate Gradient Method. Bayesian Analysis.
Continue reading
July 10, 2019
This note is based on Li Zhang, Yuan Li, & Nevatia, R. (2008). Global data association for multi-object tracking using network flows. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1–8.
Continue reading
July 16, 2019
This note is based on Campbell, N. A. (1979). CANONICAL VARIATE ANALYSIS: SOME PRACTICAL ASPECTS. 243.
Continue reading
July 17, 2019
This post is based on Ristic, B., Clark, D., & Vo, B. (2010). Improved SMC implementation of the PHD filter. 2010 13th International Conference on Information Fusion, 1–8.
Continue reading
July 17, 2019
This post is based on Li, T., Corchado, J. M., Sun, S., & Fan, H. (2017). Multi-EAP: Extended EAP for multi-estimate extraction for SMC-PHD filter. Chinese Journal of Aeronautics, 30(1), 368–379.
Continue reading
July 19, 2019
This note is based on Li, Q., & Hao, S. (2018). An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. ArXiv:1803.01299 [Cs].
Continue reading
July 21, 2019
This post is based on Li, S., Cai, T. T., & Li, H. (2019). Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach. ArXiv:1907.06116 [Stat].
Continue reading
July 21, 2019
This post is based on Ramdas, A., Zrnic, T., Wainwright, M., & Jordan, M. (2018). SAFFRON: An adaptive algorithm for online control of the false discovery rate. ArXiv:1802.09098 [Cs, Math, Stat].
Continue reading
July 23, 2019
This note is based on Chapter 13 of Nocedal, J., & Wright, S. (2006). Numerical optimization. Springer Science & Business Media.
Continue reading
July 23, 2019
This note is based on Yu, G., Bien, J., & Tibshirani, R. (2019). Reluctant Interaction Modeling. ArXiv:1907.08414 [Stat].
Continue reading
August 05, 2019
This post is based on Rossell, D., & Rubio, F. J. (2019). Additive Bayesian variable selection under censoring and misspecification. ArXiv:1907.13563 [Math, Stat].
Continue reading
August 16, 2019
Nocedal and Wright (2006) and Boyd and Vandenberghe (2004) present slightly different introduction on Interior-point method. More specifically, the former one only considers equality constraints, while the latter incorporates the inequality constraints.
Continue reading
September 08, 2019
This post is based on Section 6.4 of Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. “Statistical Learning with Sparsity,” 2016, 362.
Continue reading
This note is for Thomas, O., Dutta, R., Corander, J., Kaski, S., & Gutmann, M. U. (2016). Likelihood-free inference by ratio estimation. ArXiv:1611.10242 [Stat]. , and I got this paper from Xi’an’s blog .
Continue reading
This note is based on de Boor, C. (1978). A Practical Guide to Splines, Springer, New York.
Continue reading
September 20, 2019
This post is based on Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis (Second edition). New York, NY: Springer.
Continue reading
September 20, 2019
This note is based on the survey paper Camplani, M., Paiement, A., Mirmehdi, M., Damen, D., Hannuna, S., Burghardt, T., & Tao, L. (2016). Multiple human tracking in RGB-depth data: A survey. IET Computer Vision, 11(4), 265–285.
Continue reading
This post is based on Prof. Robert’s slides on JSM 2019 and an intuitive blog from Rasmus Bååth .
Continue reading
September 29, 2019
This note is based on Cai, T. T., Zhang, A., & Zhou, Y. (2019). Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference. ArXiv:1909.09851 [Cs, Math, Stat].
Continue reading
September 30, 2019
This note is based on Liang, T., & Rakhlin, A. (2018). Just Interpolate: Kernel “Ridgeless” Regression Can Generalize. ArXiv:1808.00387 [Cs, Math, Stat].
Continue reading
October 05, 2019
This post is based on Wainwright (2019) .
Continue reading
October 08, 2019
This post is based on Slawski, M., Diao, G., & Ben-David, E. (2019). A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data. ArXiv:1910.01623 [Cs, Stat].
Continue reading
October 10, 2019
I learnt the term Noise Outsourcing in kjytay’s blog , which is based on Teh Yee Whye’s IMS Medallion Lecture at JSM 2019.
Continue reading
October 24, 2019
I came across isotropic and anisotropic covariance functions in kjytay’s blog , and then I found more materials, chapter 4 from the book Gaussian Processes for Machine Learning , via the reference in StackExchange: What is an isotropic (spherical) covariance matrix? .
Continue reading
October 31, 2019
This post is based on Delaigle, A., & Hall, P. (2012). Methodology and theory for partial least squares applied to functional data. The Annals of Statistics, 40(1), 322–352.
Continue reading
October 31, 2019
This post is based on Lin, Z., Zamanighomi, M., Daley, T., Ma, S., & Wong, W. H. (2020). Model-Based Approach to the Joint Analysis of Single-cell Data on Chromatin Accessibility and Gene Expression. Statistical Science, 35(1), 2–13.
Continue reading
October 31, 2019
This post is based on Guo, Z., Wang, W., Cai, T. T., & Li, H. (2019). Optimal Estimation of Genetic Relatedness in High-Dimensional Linear Models. Journal of the American Statistical Association, 114(525), 358–369.
Continue reading
November 01, 2019
This note is based on Cai, T. T., Wang, Y., & Zhang, L. (2019). The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy. ArXiv:1902.04495 [Cs, Stat].
Continue reading
November 12, 2019
This post is based on Ray, N., & Acton, S. T. (2002). Active contours for cell tracking. Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, 274–278.
Continue reading
December 04, 2019
I came across the term meta-analysis in the previous post , and I had another question about nominal size while reading the paper of the previous post, which reminds me Keith’s notes . By coincidence, I also find the topic about meta-analysis in the same notes. Hence, this post is mainly based on Keith’s notes, and reproduce the power curves by myself.
Continue reading
December 06, 2019
The post is based on Jiang, Y., Neyshabur, B., Mobahi, H., Krishnan, D., & Bengio, S. (2019). Fantastic Generalization Measures and Where to Find Them. ArXiv:1912.02178 [Cs, Stat]. which was shared by one of my friend in the WeChat Moment, and then I took a quick look.
Continue reading
December 10, 2019
This post is based on Meinshausen, N. (2006). Quantile Regression Forests. 17. since a coming seminar is related to such topic.
Continue reading
December 12, 2019
This note is based on the slides of the seminar, Dr. ZHU, Huichen. Conditional Quantile Random Forest .
Continue reading
December 17, 2019
This post is based on
Peter BENTLER ’s talk, S.-Y. Lee’s Lagrange Multiplier Test in Structural Modeling: Still Useful? in the International Statistical Conference in Memory of Professor Sik-Yum Lee .
Continue reading
January 02, 2020
This post is based on the talk given by Yuchao Jiang at the 11th ICSA International Conference on Dec. 20th, 2019.
Continue reading
January 09, 2020
This post is based on the material of the first lecture of STAT6050 instructed by Prof. Wicker .
Continue reading
January 15, 2020
This post is based on the talk, given by Timothy I. Cannings at the 11th ICSA International Conference on Dec. 22th, 2019, the corresponding paper is Cannings, T. I., Fan, Y., & Samworth, R. J. (2019). Classification with imperfect training labels. ArXiv:1805.11505 [Math, Stat]
Continue reading
February 20, 2020
The first two sections are based on a good tutorial on the isotonic regression , and the third section consists of the slides for the talk given by Prof. Cun-Hui Zhang at the 11th ICSA International Conference on Dec. 21st, 2019.
Continue reading
February 24, 2020
I came across the Bernstein-von Mises theorem in Yuling Yao’s blog , and I also found a quick definition in the blog hosted by Prof. Andrew Gelman , although this one is not by Gelman. By coincidence, the former is the PhD student of the latter!
Continue reading
February 28, 2020
This post is based on Flury (1984) .
Continue reading
March 05, 2020
This note is based on Lehmann, E. L., & Romano, J. P. (2005). Testing statistical hypotheses (3rd ed). Springer.
Continue reading
April 25, 2020
This post is based on Shang, H. L. (2014). A survey of functional principal component analysis. AStA Advances in Statistical Analysis, 98(2), 121–142.
Continue reading
April 25, 2020
This post is based on Hyndman, R. J., & Shahid Ullah, Md. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 51(10), 4942–4956.
Continue reading
April 30, 2020
This post is based on Yang, C., Lu, L., Warren, J. L., Wu, J., Jiang, Q., Zuo, T., Gan, M., Liu, M., Liu, Q., DeRiemer, K., Hong, J., Shen, X., Colijn, C., Guo, X., Gao, Q., & Cohen, T. (2018). Internal migration and transmission dynamics of tuberculosis in Shanghai, China: An epidemiological, spatial, genomic analysis. The Lancet Infectious Diseases, 18(7), 788–795.
Continue reading
May 31, 2020
This note is based on the survey paper, Aminikhanghahi, S., & Cook, D. J. (2017). A Survey of Methods for Time Series Change Point Detection. Knowledge and Information Systems, 51(2), 339–367.
Continue reading
July 30, 2020
This note is for Cuturi, M., Teboul, O., Berthet, Q., Doucet, A., & Vert, J.-P. (2020). Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design.
Continue reading
August 21, 2020
This note is for Ulman, V., Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S.-Y., … Ortiz-de-Solorzano, C. (2017). An objective comparison of cell-tracking algorithms. Nature Methods, 14(12), 1141–1152.
Continue reading
August 27, 2020
This post is for Magnusson, K. E. G., Jalden, J., Gilbert, P. M., & Blau, H. M. (2015). Global Linking of Cell Tracks Using the Viterbi Algorithm. IEEE Transactions on Medical Imaging, 34(4), 911–929.
Continue reading
November 07, 2020
This note is for Besl, P. J., & McKay, N. D. (1992). Method for registration of 3-D shapes. Sensor Fusion IV: Control Paradigms and Data Structures, 1611, 586–606. .
Continue reading
This note is for Chapter 19 of Astronomy Today, 8th Edition .
Continue reading
This note is for Ye, J. (1998). On Measuring and Correcting the Effects of Data Mining and Model Selection. Journal of the American Statistical Association, 93(441), 120–131. .
Continue reading
This note is for Friedman, J. H. (1991). Multivariate Adaptive Regression Splines. The Annals of Statistics, 19(1), 1–67.
Continue reading
This note is for DAVIES, R. B. (1987). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 74(1), 33–43.
Continue reading
This note is for Hemerik, J., & Goeman, J. J. (2020). Another look at the Lady Tasting Tea and differences between permutation tests and randomization tests. International Statistical Review, insr.12431.
Continue reading
This note is for ISTR: End-to-End Instance Segmentation with Transformers .
Continue reading
This note is for Yuan, Z., Liu, Y., Yin, Q., Li, B., Feng, X., Zhang, G., & Yu, S. (2020). Unsupervised multi-granular Chinese word segmentation and term discovery via graph partition. Journal of Biomedical Informatics, 110, 103542.
Continue reading
This note is for Liu, Y., Tian, Y., Chang, T.-H., Wu, S., Wan, X., & Song, Y. (2021). Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts. Proceedings of the 20th Workshop on Biomedical Language Processing, 213–220.
Continue reading
This note is for Song, Y., Tian, Y., Wang, N., & Xia, F. (2020). Summarizing Medical Conversations via Identifying Important Utterances. Proceedings of the 28th International Conference on Computational Linguistics, 717–729.
Continue reading
This note is for Tian, Y., Shen, W., Song, Y., Xia, F., He, M., & Li, K. (2020). Improving biomedical named entity recognition with syntactic information. BMC Bioinformatics, 21(1), 539.
Continue reading
This note is for Kaski, S., Honkela, T., Lagus, K., & Kohonen, T. (1998). WEBSOM – Self-organizing maps of document collections
Continue reading
This note covers several papers on Knowledge Graph and Electronic Medical Records.
Continue reading
The note is for Milan, Anton, Stefan Roth, and Konrad Schindler. “Continuous Energy Minimization for Multitarget Tracking.” IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 1 (January 2014): 58–72.
Continue reading
This note is for Chakraborty, A., Bhattacharya, A., & Mallick, B. K. (2020). Bayesian sparse multiple regression for simultaneous rank reduction and variable selection. Biometrika, 107(1), 205–221.
Continue reading
This note is for Fang, C., He, H., Long, Q., & Su, W. J. (2021). Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training. ArXiv:2101.12699 [Cs, Math, Stat].
Continue reading
This note is for Liang, T., Rakhlin, A., & Zhai, X. (2020). On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. ArXiv:1908.10292 [Cs, Math, Stat].
Continue reading
This note is for Bartlett, P. L., Long, P. M., Lugosi, G., & Tsigler, A. (2020). Benign Overfitting in Linear Regression. ArXiv:1906.11300 [Cs, Math, Stat].
Continue reading
This note is for Magnusson, M., Andersen, M., Jonasson, J., & Vehtari, A. (2019). Bayesian leave-one-out cross-validation for large data. Proceedings of the 36th International Conference on Machine Learning, 4244–4253.
Continue reading
This note is Chi, C.-M., Vossler, P., Fan, Y., & Lv, J. (2021). Asymptotic Properties of High-Dimensional Random Forests. ArXiv:2004.13953 [Math, Stat]. .
Continue reading
The note is based on Padilha, V. A., & Campello, R. J. G. B. (2017). A systematic comparative evaluation of biclustering techniques. BMC Bioinformatics, 18(1), 55.
Continue reading
This is the note for Chernozhukov, V., Newey, W. K., Quintas-Martinez, V., & Syrgkanis, V. (2021). Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression. ArXiv:2104.14737 [Econ, Math, Stat].
Continue reading
This note is for Chipman, H. A., George, E. I., McCulloch, R. E., & Shively, T. S. (2021). mBART: Multidimensional Monotone BART. ArXiv:1612.01619 [Stat].
Continue reading
This note is for Peters, J., Bühlmann, P., & Meinshausen, N. (2016). Causal inference by using invariant prediction: Identification and confidence intervals. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(5), 947–1012.
Continue reading
This note is for Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2020). Invariant Risk Minimization. ArXiv:1907.02893 [Cs, Stat].
Continue reading
November 21, 2021
The note is for Gerber, S., & Whitaker, R. (2013). Regularization-Free Principal Curve Estimation. 18.
Continue reading
November 22, 2021
This note is for Chang, K.-Y., & Ghosh, J. (2001). A unified model for probabilistic principal surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(1), 22–41. , but only involves the principal curves.
Continue reading
December 01, 2021
This note is for Paul, D., & Aue, A. (2014). Random matrix theory in statistics: A review. Journal of Statistical Planning and Inference, 150, 1–29.
Continue reading
December 03, 2021
This note is for Austern, M., & Zhou, W. (2020). Asymptotics of Cross-Validation. ArXiv:2001.11111 [Math, Stat].
Continue reading
December 07, 2021
This note is for Buja, A., Hastie, T., & Tibshirani, R. (1989). Linear Smoothers and Additive Models. The Annals of Statistics, 17(2), 453–510. JSTOR.
Continue reading
December 13, 2021
This note is for Chapter 4 of Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
Continue reading
December 14, 2021
This note is for Chapter 3 of van Wieringen, W. N. (2021). Lecture notes on ridge regression. ArXiv:1509.09169 [Stat].
Continue reading
January 16, 2022
This note is based on Sec. 4.6 of Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed). Springer.
Continue reading
January 16, 2022
This note is for Shin, M., & Liu, J. S. (2021). Neuronized Priors for Bayesian Sparse Linear Regression. Journal of the American Statistical Association, 1–16.
Continue reading
March 14, 2022
This note is for Homrighausen, D., & McDonald, D. J. (2013). Leave-one-out cross-validation is risk consistent for lasso. ArXiv:1206.6128 [Math, Stat].
Continue reading
March 22, 2022
This note contains several papers related to scale parameter.
Continue reading
March 22, 2022
This post is for Chapter 3 of Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed). Springer.
Continue reading
March 24, 2022
The note is for Bhadra, A., Datta, J., Li, Y., Polson, N. G., & Willard, B. (2019). Prediction Risk for the Horseshoe Regression. 39.
Continue reading
March 25, 2022
This note is for scale mixture models.
Continue reading
March 25, 2022
This note is for Chen, J., Li, P., & Liu, G. (2020). Homogeneity testing under finite location-scale mixtures. Canadian Journal of Statistics, 48(4), 670–684.
Continue reading
March 30, 2022
This note is for Grandvalet, Y. (1998). Least Absolute Shrinkage is Equivalent to Quadratic Penalization. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), ICANN 98 (pp. 201–206). Springer London.
Continue reading
April 07, 2022
This note is for Meng, X.-L. (2018). Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. The Annals of Applied Statistics, 12(2).
Continue reading
April 20, 2022
This note is for Chetverikov, D. (2019). TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS. Econometric Theory, 35(4), 729–776.
Continue reading
April 20, 2022
This note is for Patton, A. J., & Timmermann, A. (2010). Monotonicity in asset returns: New tests with applications to the term structure, the CAPM, and portfolio sorts. Journal of Financial Economics, 98(3), 605–625.
Continue reading
April 23, 2022
This note is for Ghosal, S., Sen, A., & van der Vaart, A. W. (2000). Testing Monotonicity of Regression. The Annals of Statistics, 28(4), 1054–1082.
Continue reading
April 23, 2022
This note is for Wang, J. C., & Meyer, M. C. (2011). Testing the monotonicity or convexity of a function using regression splines. The Canadian Journal of Statistics / La Revue Canadienne de Statistique, 39(1), 89–107.
Continue reading
July 04, 2022
This note is for monotonic Multi-Layer Perceptron Neural network, and the references are from the R package monmlp
.
Continue reading
July 14, 2022
This note is based on Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and Biology Insights, 14, 1177932219899051.
Continue reading
July 21, 2022
This note is for Jiang, Y., & Liu, C. (2022). Estimation of Over-parameterized Models via Fitting to Future Observations (arXiv:2206.01824). arXiv.
Continue reading
September 20, 2022
This post is for the talk at Yale given by Prof. Ting Ye based on the paper Ye, T., Shao, J., & Kang, H. (2020). Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization (arXiv:1911.09802). arXiv.
Continue reading
October 06, 2022
This note is based on
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations (arXiv:2002.05709). arXiv.
Ji, W., Deng, Z., Nakada, R., Zou, J., & Zhang, L. (2021). The Power of Contrast for Feature Learning: A Theoretical Analysis (arXiv:2110.02473). arXiv.
Continue reading
October 08, 2022
This note is for Prof. Dong Xu’s talk on Wang, J., Ma, A., Chang, Y., Gong, J., Jiang, Y., Qi, R., Wang, C., Fu, H., Ma, Q., & Xu, D. (2021). ScGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Nature Communications, 12(1), Article 1.
Continue reading
October 09, 2022
This post is based on
Continue reading
October 10, 2022
This note is based on Jingyi Jessica Li’s talk on Song, D., Wang, Q., Yan, G., Liu, T., & Li, J. J. (2022). A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics (p. 2022.09.20.508796). bioRxiv.
Continue reading
October 12, 2022
This note is for Tang, D., Park, S., & Zhao, H. (2022). SCADIE: Simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure. Genome Biology, 23(1), 129.
Continue reading
October 29, 2022
This note is for Ni, Y., Stingo, F. C., Ha, M. J., Akbani, R., & Baladandayuthapani, V. (2019). Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics. Journal of the American Statistical Association, 114(525), 48–60.
Continue reading
October 30, 2022
This note is for Wang, W., Baladandayuthapani, V., Morris, J. S., Broom, B. M., Manyam, G., & Do, K.-A. (2013). iBAG: Integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics, 29(2), 149–159.
Continue reading
October 31, 2022
This note is for Cao, X., & Lee, K. (2021). Joint Bayesian Variable and DAG Selection Consistency for High-dimensional Regression Models with Network-structured Covariates. Statistica Sinica.
Continue reading
November 21, 2022
This post is based on
Continue reading
January 24, 2023
This note is based on Choi, S. W., Mak, T. S.-H., & O’Reilly, P. F. (2020). Tutorial: A guide to performing polygenic risk score analyses. Nature Protocols, 15(9), Article 9.
Continue reading
February 10, 2023
This note is for Luan, B., Lee, Y., & Zhu, Y. (2021). Predictive Model Degrees of Freedom in Linear Regression. ArXiv:2106.15682 [Math].
Continue reading
March 28, 2023
This note is for Yan, J., & Huang, J. (2012). Model Selection for Cox Models with Time-Varying Coefficients. Biometrics, 68(2), 419–428.
Continue reading
March 28, 2023
This note is for Zhang, Z., Reinikainen, J., Adeleke, K. A., Pieterse, M. E., & Groothuis-Oudshoorn, C. G. M. (2018). Time-varying covariates and coefficients in Cox regression models. Annals of Translational Medicine, 6(7), 121.
Continue reading
April 21, 2023
This note is for Schaid, D. J., Sinnwell, J. P., Batzler, A., & McDonnell, S. K. (2022). Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk. American Journal of Human Genetics, 109(5), 900–908.
Continue reading
May 05, 2023
This post is for Gandy, A., & Matcham, T. J. (2022). On concordance indices for models with time-varying risk (arXiv:2208.03213). arXiv.
Continue reading
June 29, 2023
The post is for Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), Article 12.
Continue reading
June 30, 2023
This post is for Cui, H., Wang, C., Maan, H., & Wang, B. (2023). scGPT: Towards Building a Foundation Model for Single-cell Multi-omics Using Generative AI (p. 2023.04.30.538439). bioRxiv.
Continue reading
July 10, 2023
This post is for Fanidis, D., Pezoulas, V. C., Fotiadis, D. Ι., & Aidinis, V. (2023). An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers. Computational and Structural Biotechnology Journal, 21, 2305–2315.
Continue reading
July 10, 2023
Kraven, L. M., Taylor, A. R., Molyneaux, P. L., Maher, T. M., McDonough, J. E., Mura, M., Yang, I. V., Schwartz, D. A., Huang, Y., Noth, I., Ma, S. F., Yeo, A. J., Fahy, W. A., Jenkins, R. G., & Wain, L. V. (2023). Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis. Thorax, 78(6), 551–558.
Continue reading
July 13, 2023
This post is for Natri, H. M., Azodi, C. B. D., Peter, L., Taylor, C. J., Chugh, S., Kendle, R., Chung, M., Flaherty, D. K., Matlock, B. K., Calvi, C. L., Blackwell, T. S., Ware, L. B., Bacchetta, M., Walia, R., Shaver, C. M., Kropski, J. A., McCarthy, D. J., & Banovich, N. E. (2023). Cell type-specific and disease-associated eQTL in the human lung (p. 2023.03.17.533161). bioRxiv.
Continue reading
July 27, 2023
This post is for Lin, X., Tian, T., Wei, Z., & Hakonarson, H. (2022). Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications, 13(1), Article 1.
Continue reading
July 27, 2023
The note is for Song, D., & Li, J. J. (2021). PseudotimeDE: Inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data. Genome Biology, 22(1), 124.
Continue reading
July 31, 2023
This post is for Van den Berge, K., Roux de Bézieux, H., Street, K., Saelens, W., Cannoodt, R., Saeys, Y., Dudoit, S., & Clement, L. (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications, 11(1), Article 1.
Continue reading
August 28, 2023
This note is for Craiu, R. V., Gong, R., & Meng, X.-L. (2023). Six Statistical Senses. Annual Review of Statistics and Its Application, 10(1), 699–725.
Continue reading
September 14, 2023
This note is for Garg, S., Tsipras, D., Liang, P., & Valiant, G. (2023). What Can Transformers Learn In-Context? A Case Study of Simple Function Classes (arXiv:2208.01066). arXiv.
Continue reading
September 14, 2023
The note is for Van den Berge, K., Roux de Bézieux, H., Street, K., Saelens, W., Cannoodt, R., Saeys, Y., Dudoit, S., & Clement, L. (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications, 11(1), Article 1.
Continue reading
September 21, 2023
This note is for Groeneboom, P., & Jongbloed, G. (2023). Confidence intervals in monotone regression (arXiv:2303.17988). arXiv.
Continue reading
September 21, 2023
This note is for Murray, K., Müller, S., & Turlach, B. (2016). Fast and flexible methods for monotone polynomial fitting. Journal of Statistical Computation and Simulation, 86, 1–21.
Continue reading
September 21, 2023
This note is for Papp, D., & Alizadeh, F. (2014). Shape-Constrained Estimation Using Nonnegative Splines. Journal of Computational and Graphical Statistics, 23(1), 211–231.
Continue reading
September 21, 2023
This note is for Turlach, B. A. (2005). Shape constrained smoothing using smoothing splines. Computational Statistics, 20(1), 81–104.
Continue reading
September 22, 2023
This note is for Navarro-García, M., Guerrero, V., & Durban, M. (2023). On constrained smoothing and out-of-range prediction using P-splines: A conic optimization approach. Applied Mathematics and Computation, 441, 127679.
Continue reading
October 13, 2023
This note is for Ghazanfar, Shila, Yingxin Lin, Xianbin Su, David Ming Lin, Ellis Patrick, Ze-Guang Han, John C. Marioni, and Jean Yee Hwa Yang. “Investigating Higher-Order Interactions in Single-cell Data with scHOT.” Nature Methods 17, no. 8 (August 2020): 799–806.
Continue reading
November 16, 2023
This note is for Obozinski, Guillaume, Gert Lanckriet, Charles Grant, Michael I. Jordan, and William Stafford Noble. “Consistent Probabilistic Outputs for Protein Function Prediction.” Genome Biology 9 Suppl 1, no. Suppl 1 (2008): S6.
Continue reading
November 20, 2023
This post is for two papers on Hierarchical multi-label classification (HMC), which imposes a hierarchy constraint on the classes.
Continue reading
November 25, 2023
This post is for Zhang, Shu, Ran Xu, Caiming Xiong, and Chetan Ramaiah. “Use All the Labels: A Hierarchical Multi-Label Contrastive Learning Framework,” 16660–69, 2022.
Continue reading
November 26, 2023
This post is for Pinheiro, José C., and Douglas M. Bates. “Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model.” Journal of Computational and Graphical Statistics 4, no. 1 (1995): 12–35.
Continue reading
December 04, 2023
This post is for Song, Dongyuan, Kexin Li, Xinzhou Ge, and Jingyi Jessica Li. “ClusterDE: A Post-Clustering Differential Expression (DE) Method Robust to False-Positive Inflation Caused by Double Dipping,” 2023
Continue reading
December 04, 2023
This post is for Tenha, Lovemore, and Mingzhou Song. “Statistical Evidence for the Presence of Trajectory in Single-cell Data.” BMC Bioinformatics 23, no. Suppl 8 (August 16, 2022): 340.
Continue reading
January 19, 2024
This post is for Tibshirani, R. J., Taylor, J., Lockhart, R., & Tibshirani, R. (2016). Exact Post-Selection Inference for Sequential Regression Procedures. Journal of the American Statistical Association, 111(514), 600–620.
Continue reading
January 19, 2024
This post is for Taylor, J., & Tibshirani, R. J. (2015). Statistical learning and selective inference. Proceedings of the National Academy of Sciences of the United States of America, 112(25), 7629–7634.
Continue reading
January 22, 2024
This note is for Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020). A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(5), 1273–1300.
Continue reading
January 22, 2024
This post is for Zou, Y., Carbonetto, P., Wang, G., & Stephens, M. (2022). Fine-mapping from summary data with the “Sum of Single Effects” model. PLOS Genetics, 18(7), e1010299.
Continue reading
January 23, 2024
The post is for Lee, A. F. S., & Gurland, J. (1977). One-Sample t-Test When Sampling from a Mixture of Normal Distributions. The Annals of Statistics, 5(4), 803–807.
Continue reading
January 24, 2024
This note is based on Shao, J. (2003). Mathematical statistics (2nd ed). Springer. and Hwang, J. (2019). Note on Edgeworth Expansions and Asymptotic Refinements of Percentile t-Bootstrap Methods. Bootstrap Methods.
Continue reading
February 07, 2024
This note is for Loh, P.-R., Bhatia, G., Gusev, A., Finucane, H. K., Bulik-Sullivan, B. K., Pollack, S. J., de Candia, T. R., Lee, S. H., Wray, N. R., Kendler, K. S., O’Donovan, M. C., Neale, B. M., Patterson, N., & Price, A. L. (2015). Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis. Nature Genetics, 47(12), 1385–1392.
Continue reading
February 08, 2024
This note is for Gao, L. L., Bien, J., & Witten, D. (2022). Selective Inference for Hierarchical Clustering (arXiv:2012.02936). arXiv.
Continue reading
February 08, 2024
This post is for Gaynor, S. M., Fagny, M., Lin, X., Platig, J., & Quackenbush, J. (2022). Connectivity in eQTL networks dictates reproducibility and genomic properties. Cell Reports Methods, 2(5), 100218.
Continue reading
February 08, 2024
This post is for González-Delgado, J., Cortés, J., & Neuvial, P. (2023). Post-clustering Inference under Dependency (arXiv:2310.11822). arXiv.
Continue reading
February 09, 2024
The note is for Spector, A., & Janson, L. (2023). Controlled Discovery and Localization of Signals via Bayesian Linear Programming (arXiv:2203.17208). arXiv.
Continue reading
March 26, 2024
This post is for Ahlmann-Eltze, C., & Huber, W. (2023). Comparison of transformations for single-cell RNA-seq data. Nature Methods, 20(5), 665–672.
Continue reading
April 12, 2024
This note is for Chen, Y. T., & Witten, D. M. (2022). Selective inference for k-means clustering (arXiv:2203.15267). arXiv.
Continue reading
This note is for Chen, Y. T., & Gao, L. L. (2023). Testing for a difference in means of a single feature after clustering (arXiv:2311.16375). arXiv.
Continue reading
This note is for Boyeau, P., Bates, S., Ergen, C., Jordan, M. I., & Yosef, N. (2023). Calibrated Identification of Feature Dependencies in Single-cell Multiomics.
Continue reading
April 30, 2024
This note is for Mason, K., Sathe, A., Hess, P. R., Rong, J., Wu, C.-Y., Furth, E., Susztak, K., Levinsohn, J., Ji, H. P., & Zhang, N. (2024). Niche-DE: Niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions. Genome Biology, 25(1), 14.
Continue reading
This note is for Chen, Z., He, Z., Chu, B. B., Gu, J., Morrison, T., Sabatti, C., & Candès, E. (2024). Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression (arXiv:2402.12724). arXiv.
Continue reading
This note is for Sun, E. D., Ma, R., Navarro Negredo, P., Brunet, A., & Zou, J. (2024). TISSUE: Uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. Nature Methods, 21(3), 444–454.
Continue reading
This note is for Neufeld, A., Dharamshi, A., Gao, L. L., & Witten, D. (2024). Data Thinning for Convolution-Closed Distributions. Journal of Machine Learning Research, 25(57), 1–35.
Continue reading
See all posts →