Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet proces...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2022-10, Vol.24 (10), p.1466
Main Authors: Tang, Anmin, Duan, Xingde, Zhao, Yuanying
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Approximation
Bayesian analysis
Bayesian Lasso
Dirichlet problem
Dirichlet process prior
Feature selection
Gibbs sampler
Metropolis–Hastings algorithm
Normal distribution
Random variables
Regression analysis
Regularization methods
simplex distribution
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Summary:In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis-Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.
ISSN:1099-4300
1099-4300
DOI:10.3390/e24101466