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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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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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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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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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SLOPE - Adaptive variable selection via convex optimization [PDF]
, 2015 We introduce a new estimator for the vector of coefficients $\beta$ in the linear model $y=X\beta+z$, where $X$ has dimensions $n\times p$ with $p$ possibly larger than $n$. SLOPE, short for Sorted L-One Penalized Estimation, is the solution to \[\min_{b\
Berg, Ewout van den +4 more
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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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Variable Selection and Parameter Tuning in High-Dimensional Prediction [PDF]
, 2010 In the context of classification using high-dimensional data such as microarray gene expression data, it is often useful to perform preliminary variable selection.
Bernau, Christoph +1 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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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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