Frist Glance at KEGGgraph
November 21, 2022
This post is based on
- Zhang JD, Wiemann S (2022-11-01). KEGGgraph: a graph approach to KEGG PATHWAY in R and Bioconductor.
- Zhang JD (2022-11-01). KEGGgraph: Application Examples
Joint Local False Discovery Rate in GWAS
November 12, 2022 (Update: )
Differentiable Sorting and Ranking
November 04, 2022 (Update: )
This note is for Blondel, M., Teboul, O., Berthet, Q., & Djolonga, J. (2020). Fast Differentiable Sorting and Ranking (arXiv:2002.08871). arXiv.
Joint Bayesian Variable and DAG Selection
October 31, 2022
Integrative Bayesian Analysis of High-dimensional Multiplatform Genomics Data
October 30, 2022
Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics.
October 29, 2022
Simultaneous Estimation of Cell Type Proportions and Cell Type-specific Gene Expressions
October 12, 2022
scDesign3: A Single-cell Simulator
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.
ADAM and AMSGrad for Stochastic Optimization
October 09, 2022
This post is based on
- Kingma, D. P., & Ba, J. (2017). Adam: A Method for Stochastic Optimization (arXiv:1412.6980). arXiv.
- Reddi, S. J., Kale, S., & Kumar, S. (2018, February 15). On the Convergence of Adam and Beyond. International Conference on Learning Representations.
Single-cell Graph Neural Network
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.
Contrastive Learning: A Simple Framework and A Theoretical Analysis
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.
Joint Model of Longitudinal and Survival Data
October 02, 2022 (Update: )
This post is based on Rizopoulos, D. (2017). An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R. 235.
Debiased Inverse-Variance Weighted Estimator in Mendelian Randomization
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.
Multicenter IPF-PRO Registry Cohort
August 25, 2022 (Update: )
Fitting to Future Observations
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.
Machine Learning for Multi-omics Data
July 15, 2022 (Update: )
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.
Review on Multi-omics Data
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.
Monotone Multi-Layer Perceptron
July 04, 2022
This note is for monotonic Multi-Layer Perceptron Neural network, and the references are from the R package monmlp.
Test of Monotonicity by Calibrating for Linear Functions
May 11, 2022 (Update: )
Test of Monotonicity and Convexity by Splines
April 23, 2022
Test of Monotonicity by U-processes
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.
Monotonicity in Asset Returns
April 20, 2022
Test of Monotonicity
April 20, 2022
This note is for Chetverikov, D. (2019). TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS. Econometric Theory, 35(4), 729–776.
Big Data Paradox
April 07, 2022
Adaptive Ridge Estimate
March 30, 2022
Mixture of Location-Scale Families
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.
Scale Mixture Models
March 25, 2022
This note is for scale mixture models.
Prediction Risk for the Horseshoe Regression
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.
Estimation of Location and Scale Parameters of Continuous Density
March 22, 2022 (Update: )
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.
Equivariance
March 22, 2022
This post is for Chapter 3 of Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed). Springer.
Applications with Scale Parameters
March 22, 2022
This note contains several papers related to scale parameter.
Leave-one-out CV for Lasso
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].
Neuronized Priors for Bayesian Sparse Linear Regression
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.
Empirical Bayes
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.
Magnetic Field Orientations in Star Formation
January 12, 2022
Generalizing Ridge Regression
December 14, 2021
This note is for Chapter 3 of van Wieringen, W. N. (2021). Lecture notes on ridge regression. ArXiv:1509.09169 [Stat].
Gaussian Processes for Regression
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.
Additive Model with Linear Smoother
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.
Asymptotics of Cross Validation
December 03, 2021
This note is for Austern, M., & Zhou, W. (2020). Asymptotics of Cross-Validation. ArXiv:2001.11111 [Math, Stat].
Review on Random Matrix Theory
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.
Probabilistic Principal Curves
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.
Regularization-Free Principal Curves
November 21, 2021
The note is for Gerber, S., & Whitaker, R. (2013). Regularization-Free Principal Curve Estimation. 18.
Invariant Risk Minimization
November 19, 2021 0 Comments
This note is for Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2020). Invariant Risk Minimization. ArXiv:1907.02893 [Cs, Stat].
Causal Inference by Invariant Prediction
November 19, 2021 0 Comments
Infinite Relational Model
November 18, 2021 (Update: ) 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].
Multidimensional Monotone Bayesian Additive Regression Tree
November 17, 2021 0 Comments
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].
Debiased ML via NN for GLM
November 16, 2021 0 Comments
Biclustering on Gene Expression Data
November 10, 2021 0 Comments
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.