November 04, 2024
This note is for Niu, Z., Choudhury, J. R., & Katsevich, E. (2024). Computationally efficient and statistically accurate conditional independence testing with spaCRT (No. arXiv:2407.08911; Version 1). arXiv. https://doi.org/10.48550/arXiv.2407.08911
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November 01, 2024 (Update: )
This is the note for Moreau, T., Massias, M., Gramfort, A., Ablin, P., Bannier, P.-A., Charlier, B., Dagréou, M., Tour, T. D. la, Durif, G., Dantas, C. F., Klopfenstein, Q., Larsson, J., Lai, E., Lefort, T., Malézieux, B., Moufad, B., Nguyen, B. T., Rakotomamonjy, A., Ramzi, Z., … Vaiter, S. (2022). Benchopt: Reproducible, efficient and collaborative optimization benchmarks (No. arXiv:2206.13424). arXiv. https://doi.org/10.48550/arXiv.2206.13424
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October 04, 2024
This post is based on He, J., Yalov, S., & Hahn, P. R. (2019). XBART: Accelerated Bayesian Additive Regression Trees. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 1130–1138. https://proceedings.mlr.press/v89/he19a.html and He, J., & Hahn, P. R. (2023). Stochastic Tree Ensembles for Regularized Nonlinear Regression. Journal of the American Statistical Association, 118(541), 551–570. https://doi.org/10.1080/01621459.2021.1942012
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September 10, 2024 (Update: ) 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
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September 10, 2024 (Update: ) 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
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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.
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August 05, 2024 (Update: ) 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
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August 05, 2024 (Update: ) 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
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August 05, 2024 (Update: )
This is the note for the talk LLMs training given by Linjun Zhang at JSM 2024
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August 05, 2024 (Update: )
This is the note for the talk Statistical Inference in Large Language Models: Alignment and Copyright given by Weijie Su at JSM 2024
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This note is for Wainwright, M. J. (n.d.). High-Dimensional Statistics: A Non-Asymptotic Viewpoint. 604.
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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
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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.
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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.
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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.
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April 20, 2024 (Update: )
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].
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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.
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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.
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April 12, 2024
This note is for Chen, Y. T., & Witten, D. M. (2022). Selective inference for k-means clustering (arXiv:2203.15267). arXiv.
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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.
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February 24, 2024 (Update: )
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.
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February 20, 2024 (Update: )
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.
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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.
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February 08, 2024
This post is for González-Delgado, J., Cortés, J., & Neuvial, P. (2023). Post-clustering Inference under Dependency (arXiv:2310.11822). arXiv.
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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.
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February 08, 2024
This note is for Gao, L. L., Bien, J., & Witten, D. (2022). Selective Inference for Hierarchical Clustering (arXiv:2012.02936). arXiv.
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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.
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January 31, 2024 (Update: )
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.
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January 31, 2024 (Update: )
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.
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January 26, 2024 (Update: )
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.
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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.
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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.
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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.
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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.
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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.
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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.
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January 15, 2024 (Update: )
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.
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January 10, 2024 (Update: )
This post is based on vignettes of MMRM R package: https://openpharma.github.io/mmrm/main/index.html
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January 06, 2024 (Update: )
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.
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December 08, 2023 (Update: )
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.
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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.
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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
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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.
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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.
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November 20, 2023
This post is for two papers on Hierarchical multi-label classification (HMC), which imposes a hierarchy constraint on the classes.
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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.
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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.
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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.
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September 21, 2023
This note is for Turlach, B. A. (2005). Shape constrained smoothing using smoothing splines. Computational Statistics, 20(1), 81–104.
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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.
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