83 citations · 83 across the 2 of their papers we have counts for
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Pre-processing with Orthogonal Decompositions for High-dimensional Explanatory Variables
Xu Han, Ethan X Fang, Cheng Yong Tang
Strong correlations between explanatory variables are problematic for high-dimensional regularized regression methods. Due to the violation of the Irrepresentable Condition, the po…
Adjusting the Benjamini-Hochberg method for controlling the false discovery rate in knockoff assisted variable selection
Sanat K. Sarkar, Cheng Yong Tang
The knockoff-based multiple testing setup of Barber & Candes (2015) for variable selection in multiple regression where sample size is as large as the number of explanatory variabl…
Missing at Random or Not: A Semiparametric Testing Approach
Rui Duan, C. Jason Liang, Pamela Shaw +2
Practical problems with missing data are common, and statistical methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus…
High-dimensional Interactions Detection with Sparse Principal Hessian Matrix
Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong
In statistical learning framework with regressions, interactions are the contributions to the response variable from the products of the explanatory variables. In high-dimensional…
Tuning parameter selection in high dimensional penalized likelihood
Yingying Fan, Cheng Yong Tang
Determining how to appropriately select the tuning parameter is essential in penalized likelihood methods for high-dimensional data analysis. We examine this problem in the setting…