Rademacher Complexity
January 16, 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).
Classification with Imperfect Training Labels
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]
NGS for NGS
January 11, 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.
Concentration Inequality for Machine Learning
January 09, 2020
This post is based on the material of the first lecture of STAT6050 instructed by Prof. Wicker.
DNA copy number profiling: from bulk tissue to single cells
January 02, 2020
This post is based on the talk given by Yuchao Jiang at the 11th ICSA International Conference on Dec. 20th, 2019.
CEASE
December 20, 2019
This post is based on the Peter Hall Lecture given by Prof. Jianqing Fan at the 11th ICSA International Conference on Dec. 20th, 2019.
Group Inference in High Dimensions
December 17, 2019
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
Lagrange Multiplier Test
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.
Conditional Quantile Regression Forests
December 12, 2019
This note is based on the slides of the seminar, Dr. ZHU, Huichen. Conditional Quantile Random Forest.
Quantile Regression Forests
December 10, 2019
This post is based on Meinshausen, N. (2006). Quantile Regression Forests. 17. since a coming seminar is related to such topic.
Fantastic Generalization Measures and Where to Find Them
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.
Combining $p$-values in Meta Analysis
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.
Controlling bias and inflation in EWAS/TWAS
December 04, 2019
Union-intersection tests and Intersection-union tests
December 02, 2019
This post is based on section 8.3 of Casella and Berger (2001).
Gaussian DAGs on Network Data
November 19, 2019
This post is based on Li, H., & Zhou, Q. (2019). Gaussian DAGs on network data. ArXiv:1905.10848 [Cs, Stat].
Active Contours
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.
Multivariate Mediation Effects
November 04, 2019
This note is based on Huang, Y.-T. (2019). Variance component tests of multivariate mediation effects under composite null hypotheses. Biometrics, 0(0).
The Cost of Privacy
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].
MM algorithm for Variance Components Models
November 01, 2019
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.
Genetic Relatedness in High-Dimensional Linear Models
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.
Model-based Approach for Joint Analysis of Single-cell data
October 31, 2019
This post is based on Lin Z†, Zamanighomi M, Daley T, Ma S and Wong WH†: Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression. Statistical Science
Partial Least Squares for Functional Data
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.
Generalized Functional Linear Models with Semiparametric Single-index Interactions
October 29, 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.
Isotropic vs. Anisotropic
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?.
Noise Outsourcing
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.
Linear Regression with Partially Shuffled Data
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].
Sub Gaussian
October 05, 2019
This post is based on Wainwright (2019).
Kernel Ridgeless Regression Can Generalize
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].
Optimality for Sparse Group Lasso
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].
ABC for Socks
September 24, 2019 0 Comments
This post is based on Prof. Robert’s slides on JSM 2019 and an intuitive blog from Rasmus Bååth.