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Variable selection in multivariate multiple regression.
PLoS ONE, 2020 IntroductionIn 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.
Asokan Mulayath Variyath, Anita Brobbey
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Fault Relevant Variable Selection for Fault Diagnosis
IEEE Access, 2020 In process monitoring, fault relevant variable selection and fault diagnosis are two important branches. But they are often discussed independently and scarcely integrated in research.
Ruixiang Deng +2 more
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Variable Selection in General Multinomial Logit Models [PDF]
, 2012 The use of the multinomial logit model is typically restricted to applications with few predictors, because in high-dimensional settings maximum likelihood estimates tend to deteriorate.
Pößnecker, Wolfgang +2 more
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Group Variable Selection Methods with Quantile Regression: A Simulation Study. [PDF]
المجلة العراقية للعلوم الاحصائيةIn many cases, covariates have a grouping structure that can be used in the analysis to identify important groups and the significant members of those groups. This paper reviews some group variable selection methods that utilize quantile regression.
Hussein Hashem
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IEEE Access, 2022 Population health is multidimensional in nature, having complex relationships with the various health determinants. However, most previous studies investigate a single dimension of population health using linear models, failing to capture the ...
Zhiyuan Wei +2 more
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Beyond Support in Two-Stage Variable Selection [PDF]
, 2015 Numerous variable selection methods rely on a two-stage procedure, where a sparsity-inducing penalty is used in the first stage to predict the support, which is then conveyed to the second stage for estimation or inference purposes.
Ambroise, Christophe +3 more
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Multinomial Logit Models with Implicit Variable Selection [PDF]
, 2010 Multinomial logit models which are most commonly used for the modeling of unordered multi-category responses are typically restricted to the use of few predictors. In the high-dimensional case maximum likelihood estimates frequently do not exist. In this
Tutz, Gerhard, Zahid, Faisal Maqbool
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Environmental Health, 2017 Background There is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures ...
Jose Barrera-Gómez +14 more
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Bayesian Criterion-Based Variable Selection
Journal of the Royal Statistical Society Series C: Applied Statistics, 2021 AbstractBayesian approaches for criterion based selection include the marginal likelihood based highest posterior model (HPM) and the deviance information criterion (DIC). The DIC is popular in practice as it can often be estimated from sampling-based methods with relative ease and DIC is readily available in various Bayesian software.
Maity, Arnab Kumar +2 more
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Analysis of Information-Based Nonparametric Variable Selection Criteria
Entropy, 2020 We consider a nonparametric Generative Tree Model and discuss a problem of selecting active predictors for the response in such scenario. We investigated two popular information-based selection criteria: Conditional Infomax Feature Extraction (CIFE) and ...
Małgorzata Łazęcka, Jan Mielniczuk
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