Predictive Variable Selection for the Multivariate Linear Model

Biometrics ◽  
1997 ◽  
Vol 53 (2) ◽  
pp. 465 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Ming-Hui Chen
Keyword(s):  
Variable Selection ◽  
Linear Model ◽  
Multivariate Linear Model ◽  
Predictive Variable ◽  
Selection For

scholarly journals Quantile regression and variable selection for partially linear model with randomly truncated data

2017 ◽  
Vol 60 (4) ◽  
pp. 1137-1160 ◽  
Author(s):  
Hong-Xia Xu ◽  
Zhen-Long Chen ◽  
Jiang-Feng Wang ◽  
Guo-Liang Fan
Keyword(s):  
Variable Selection ◽  
Quantile Regression ◽  
Linear Model ◽  
Partially Linear Model ◽  
Truncated Data ◽  
Partially Linear ◽  
Selection For

A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data

2018 ◽  
Vol 17 (5) ◽  
Author(s):  
Marie Perrot-Dockès ◽  
Céline Lévy-Leduc ◽  
Julien Chiquet ◽  
Laure Sansonnet ◽  
Margaux Brégère ◽  
...  
Keyword(s):  
Variable Selection ◽  
Linear Model ◽  
Random Error ◽  
Strong Dependence ◽  
Dependence Structure ◽  
Multivariate Linear Model ◽  
Model Framework ◽  
Data Set ◽  
Metabolomics Data ◽  
Error Matrix

Abstract Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. Applying statistical procedures that do not adjust the variable selection step to the dependence pattern may result in a loss of power and the selection of spurious variables. The goal of this paper is to propose a variable selection procedure within the multivariate linear model framework that accounts for the dependence between the multiple responses. We shall focus on a specific type of dependence which consists in assuming that the responses of a given individual can be modelled as a time series. We propose a novel Lasso-based approach within the framework of the multivariate linear model taking into account the dependence structure by using different types of stationary processes covariance structures for the random error matrix. Our numerical experiments show that including the estimation of the covariance matrix of the random error matrix in the Lasso criterion dramatically improves the variable selection performance. Our approach is successfully applied to an untargeted LC-MS (Liquid Chromatography-Mass Spectrometry) data set made of African copals samples. Our methodology is implemented in the R package MultiVarSel which is available from the Comprehensive R Archive Network (CRAN).

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For
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