Bayesian model averaging in longitudinal regression models with AR(1) errors with application to a myopia data set

Tsai Hung Fan, Guo Tzau Wang

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a new iterative algorithm, called model walking algorithm, to the Bayesian model averaging method on the longitudinal regression models with AR(1) random errors within subjects. The Markov chain Monte Carlo method together with the model walking algorithm are employed. The proposed method is successfully applied to predict the progression rates on a myopia intervention trial in children.

Original languageEnglish
Pages (from-to)1667-1678
Number of pages12
JournalJournal of Statistical Computation and Simulation
Volume85
Issue number8
DOIs
StatePublished - 24 May 2015

Keywords

  • Bayesian model averaging
  • MCMC
  • longitudinal regression model
  • model walking algorithm

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