Determination of correlations in multivariate longitudinal data with modified Cholesky and hypersphere decomposition using Bayesian variable selection approach

Kuo Jung Lee, Ray Bing Chen, Min Sun Kwak, Keunbaik Lee

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, we present a Bayesian framework for multivariate longitudinal data analysis with a focus on selection of important elements in the generalized autoregressive matrix. An efficient Gibbs sampling algorithm was developed for the proposed model and its implementation in a comprehensive R package called MLModelSelection is available on the comprehensive R archive network. The performance of the proposed approach was studied via a comprehensive simulation study. The effectiveness of the methodology was illustrated using a nonalcoholic fatty liver disease dataset to study correlations in multiple responses over time to explain the joint variability of lung functions and body mass index. Supplementary materials for this article, including a standardized description of the materials needed to reproduce the work, are available as an online supplement.

Original languageEnglish
Pages (from-to)978-997
Number of pages20
JournalStatistics in Medicine
Volume40
Issue number4
DOIs
StatePublished - 20 Feb 2021

Keywords

  • Gibbs sampling
  • R package
  • lung function
  • nonalcoholic fatty liver disease
  • positive definiteness
  • serial correlation matrix

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