An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis

In this paper, we propose a variable selection procedure for partially linear varying coefficient model under quantile loss function with adaptive Lasso penalty. The functional coefficients are estimated by B-spline approximations. The proposed procedure simultaneously selects significant variables and estimates unknown parameters. The major advantage of the proposed procedures over the existing ones is easy to implement using existing software, and it requires no specification of the error distributions. Under the regularity conditions, we show that the proposed procedure can be as efficient as the Oracle estimator, and derive the optimal convergence rate of the functional coefficients. A simulation study and a real data application are undertaken to assess the finite sample performance of the proposed variable selection procedure.

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Robust Adaptive Lasso method for parameter’s estimation and variable selection in high-dimensional sparse models

PLoS ONE ◽

10.1371/journal.pone.0183518 ◽

2017 ◽

Vol 12 (8) ◽

pp. e0183518 ◽

Cited By ~ 3

Author(s):

Abdul Wahid ◽

Dost Muhammad Khan ◽

Ijaz Hussain

Keyword(s):

Variable Selection ◽

Adaptive Lasso ◽

High Dimensional ◽

Sparse Models ◽

Robust Adaptive ◽

Lasso Method

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Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2017.1387663 ◽

2018 ◽

Vol 48 (2) ◽

pp. 530-543 ◽

Cited By ~ 2

Author(s):

Dao Thanh Tung ◽

Minh-Ngoc Tran ◽

Tran Manh Cuong

Keyword(s):

Variable Selection ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Variational Bayes ◽

Adaptive Lasso ◽

High Dimensional

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A pseudo knockoff filter for correlated features

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iay012 ◽

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.

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