Variable selection in multivariate multiple regression.

<h4>Introduction</h4>In many practical situations, we are interested in the effect of covariates on correlated multiple responses. In this paper, we focus on estimation and variable selection in multi-response multiple regression models. Correlation among the response variables must be m...

Full description

Bibliographic Details
Main Authors: Asokan Mulayath Variyath, Anita Brobbey
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0236067
_version_ 1819113984278659072
author Asokan Mulayath Variyath
Anita Brobbey
author_facet Asokan Mulayath Variyath
Anita Brobbey
author_sort Asokan Mulayath Variyath
collection DOAJ
description <h4>Introduction</h4>In many practical situations, we are interested in the effect of covariates on correlated multiple responses. In this paper, we focus on estimation and variable selection in multi-response multiple regression models. Correlation among the response variables must be modeled for valid inference.<h4>Method</h4>We used an extension of the generalized estimating equation (GEE) methodology to simultaneously analyze binary, count, and continuous outcomes with nonlinear functions. Variable selection plays an important role in modeling correlated responses because of the large number of model parameters that must be estimated. We propose a penalized-likelihood approach based on the extended GEEs for simultaneous parameter estimation and variable selection.<h4>Results and conclusions</h4>We conducted a series of Monte Carlo simulations to investigate the performance of our method, considering different sample sizes and numbers of response variables. The results showed that our method works well compared to treating the responses as uncorrelated. We recommend using an unstructured correlation model with the Bayesian information criterion (BIC) to select the tuning parameters. We demonstrated our method using data from a concrete slump test.
first_indexed 2024-12-22T04:38:05Z
format Article
id doaj.art-5f880c3a1f9c48f48e170f4f969ba179
institution Directory Open Access Journal
issn 1932-6203
language English
last_indexed 2024-12-22T04:38:05Z
publishDate 2020-01-01
publisher Public Library of Science (PLoS)
record_format Article
series PLoS ONE
spelling doaj.art-5f880c3a1f9c48f48e170f4f969ba1792022-12-21T18:38:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01157e023606710.1371/journal.pone.0236067Variable selection in multivariate multiple regression.Asokan Mulayath VariyathAnita Brobbey<h4>Introduction</h4>In many practical situations, we are interested in the effect of covariates on correlated multiple responses. In this paper, we focus on estimation and variable selection in multi-response multiple regression models. Correlation among the response variables must be modeled for valid inference.<h4>Method</h4>We used an extension of the generalized estimating equation (GEE) methodology to simultaneously analyze binary, count, and continuous outcomes with nonlinear functions. Variable selection plays an important role in modeling correlated responses because of the large number of model parameters that must be estimated. We propose a penalized-likelihood approach based on the extended GEEs for simultaneous parameter estimation and variable selection.<h4>Results and conclusions</h4>We conducted a series of Monte Carlo simulations to investigate the performance of our method, considering different sample sizes and numbers of response variables. The results showed that our method works well compared to treating the responses as uncorrelated. We recommend using an unstructured correlation model with the Bayesian information criterion (BIC) to select the tuning parameters. We demonstrated our method using data from a concrete slump test.https://doi.org/10.1371/journal.pone.0236067
spellingShingle Asokan Mulayath Variyath
Anita Brobbey
Variable selection in multivariate multiple regression.
PLoS ONE
title Variable selection in multivariate multiple regression.
title_full Variable selection in multivariate multiple regression.
title_fullStr Variable selection in multivariate multiple regression.
title_full_unstemmed Variable selection in multivariate multiple regression.
title_short Variable selection in multivariate multiple regression.
title_sort variable selection in multivariate multiple regression
url https://doi.org/10.1371/journal.pone.0236067
work_keys_str_mv AT asokanmulayathvariyath variableselectioninmultivariatemultipleregression
AT anitabrobbey variableselectioninmultivariatemultipleregression