Distributed inference for quantile regression processes
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.
The Correlated Topic Model
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.
Tweedie's Formula and Selection Bias
March 11, 2019
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.
Bernstein Bounds
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).
Evaluate Variational Inference
March 07, 2019
A brief summary of the post, Eid ma clack shaw zupoven del ba.
Tensor Completion
March 07, 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.
Illustrate Path Sampling by Stan Programming
March 06, 2019 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.
Select Prior by Formal Rules
March 04, 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.
Bootstrap Hypothesis Testing
March 03, 2019 0 Comments
This report is motivated by comments under Larry’s post, Modern Two-Sample Tests.
Bio-chemical Reaction Networks
February 25, 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.
SMC for Protein Folding Problem
February 23, 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.
Stein's Paradox
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)
Continuous Time Markov Chain
February 20, 2019
This note is based on Karl Sigman’s IEOR 6711: Continuous-Time Markov Chains.
Restricted Isometry Property
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.
Presistency
February 18, 2019
The paper, Greenshtein and Ritov (2004), is recommended by Larry Wasserman in his post Consistency, Sparsistency and Presistency.
A Bayesian Perspective of Deep Learning
February 17, 2019
This note is for Polson, N. G., & Sokolov, V. (2017). Deep Learning: A Bayesian Perspective. Bayesian Analysis, 12(4), 1275–1304.
Deep Learning
February 16, 2019
This note is based on LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Studentized U-statistic
February 15, 2019 0 Comments
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.
Review of Composite Likelihood
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.
Gibbs Sampling for the Multivariate Normal
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.
Identification of PE Genes in Cell Cycle
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.
Comparisons of Three Likelihood Criteria
February 12, 2019
The First Glimpse into Pseudolikelihood
February 12, 2019
This post caught a glimpse of the pseudolikelihood.
Calculating Marginal likelihood
January 30, 2019
Annealed Importance Sampling
January 28, 2019
This is the note for Neal, R. M. (1998). Annealed Importance Sampling. ArXiv:Physics/9803008.
Annealed SMC for Bayesian Phylogenetics
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].
The Kalman Filter and Extended Kalman Filter
January 21, 2019
Sequential Monte Carlo Methods
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.
Particle Filtering and Smoothing
January 18, 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.
PLS in High-Dimensional Regression
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.