scholarly journals A pseudo knockoff filter for correlated features

2018 ◽  
Vol 8 (2) ◽  
pp. 313-341
Author(s):  
Jiajie Chen ◽  
Anthony Hou ◽  
Thomas Y Hou
Keyword(s):  
Variable Selection ◽  
False Discovery Rate ◽  
Numerical Experiments ◽  
Selection Procedure ◽  
Numerical Examples ◽  
False Discovery ◽  
Variable Selection Procedure ◽  
Partial Analysis ◽  
False Discovery Proportion

Abstract In Barber & Candès (2015, Ann. Statist., 43, 2055–2085), the authors introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. Inspired by the work by Barber & Candès (2015, Ann. Statist., 43, 2055–2085), we propose a pseudo knockoff filter that inherits some advantages of the original knockoff filter and has more flexibility in constructing its knockoff matrix. Moreover, we perform a number of numerical experiments that seem to suggest that the pseudo knockoff filter with the half Lasso statistic has FDR control and offers more power than the original knockoff filter with the Lasso Path or the half Lasso statistic for the numerical examples that we consider in this paper. Although we cannot establish rigourous FDR control for the pseudo knockoff filter, we provide some partial analysis of the pseudo knockoff filter with the half Lasso statistic and establish a uniform false discovery proportion bound and an expectation inequality.

scholarly journals A prototype knockoff filter for group selection with FDR control

2019 ◽  
Vol 9 (2) ◽  
pp. 271-288 ◽  
Author(s):  
Jiajie Chen ◽  
Anthony Hou ◽  
Thomas Y Hou
Keyword(s):  
Machine Learning ◽  
Group Selection ◽  
Numerical Experiments ◽  
Statistical Power ◽  
Selection Procedure ◽  
Principal Component ◽  
Response Variable ◽  
Explanatory Variables ◽  
False Discovery ◽  
Variable Selection Procedure

Abstract In many applications, we need to study a linear regression model that consists of a response variable and a large number of potential explanatory variables, and determine which variables are truly associated with the response. In Foygel Barber & Candès (2015, Ann. Statist., 43, 2055–2085), the authors introduced a new variable selection procedure called the knockoff filter to control the false discovery rate (FDR) and proved that this method achieves exact FDR control. In this paper, we propose a prototype knockoff filter for group selection by extending the Reid–Tibshirani (2016, Biostatistics, 17, 364–376) prototype method. Our prototype knockoff filter improves the computational efficiency and statistical power of the Reid–Tibshirani prototype method when it is applied for group selection. In some cases when the group features are spanned by one or a few hidden factors, we demonstrate that the Principal Component Analysis (PCA) prototype knockoff filter outperforms the Dai–Foygel Barber (2016, 33rd International Conference on Machine Learning (ICML 2016)) group knockoff filter. We present several numerical experiments to compare our prototype knockoff filter with the Reid–Tibshirani prototype method and the group knockoff filter. We have also conducted some analysis of the knockoff filter. Our analysis reveals that some knockoff path method statistics, including the Lasso path statistic, may lead to loss of power for certain design matrices and a specially designed response even if their signal strengths are still relatively strong.

scholarly journals A Variable Selection Procedure for X-ray Diffraction Phase Analysis

2007 ◽  
Vol 61 (12) ◽  
pp. 1398-1403 ◽  
Author(s):  
Daewon Lee ◽  
Hyeseon Lee ◽  
Chi-Hyuck Jun ◽  
Chang Hwan Chang
Keyword(s):  
Variable Selection ◽  
Phase Analysis ◽  
Selection Procedure ◽  
X Ray Diffraction ◽  
X Ray ◽  
Variable Selection Procedure

scholarly journals Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

2020 ◽  
Vol 3 (1) ◽  
pp. 66-80 ◽  
Author(s):  
Sierra A. Bainter ◽  
Thomas G. McCauley ◽  
Tor Wager ◽  
Elizabeth A. Reynolds Losin
Keyword(s):  
Variable Selection ◽  
Multiple Testing ◽  
Selection Procedure ◽  
Experimental Pain ◽  
Bayesian Variable Selection ◽  
Large Set ◽  
Web Based ◽  
Stochastic Search Variable Selection ◽  
Variable Selection Procedure ◽  
Multivariate Relationships

Frequently, researchers in psychology are faced with the challenge of narrowing down a large set of predictors to a smaller subset. There are a variety of ways to do this, but commonly it is done by choosing predictors with the strongest bivariate correlations with the outcome. However, when predictors are correlated, bivariate relationships may not translate into multivariate relationships. Further, any attempts to control for multiple testing are likely to result in extremely low power. Here we introduce a Bayesian variable-selection procedure frequently used in other disciplines, stochastic search variable selection (SSVS). We apply this technique to choosing the best set of predictors of the perceived unpleasantness of an experimental pain stimulus from among a large group of sociocultural, psychological, and neurobiological (functional MRI) individual-difference measures. Using SSVS provides information about which variables predict the outcome, controlling for uncertainty in the other variables of the model. This approach yields new, useful information to guide the choice of relevant predictors. We have provided Web-based open-source software for performing SSVS and visualizing the results.

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