Semiparametric Bayes local additive models for longitudinal data.

Published

Journal Article

In longitudinal data analysis, there is great interest in assessing the impact of predictors on the time-varying trajectory in a response variable. In such settings, an important issue is to account for heterogeneity in the shape of the trajectory among subjects, while allowing the impact of the predictors to vary across subjects. We propose a flexible semiparametric Bayes approach for addressing this issue relying on a local partition process prior, which allows flexible local borrowing of information across subjects. Local hypothesis testing and credible bands are developed for the identification of time windows across which a predictor has a significant impact, while adjusting for multiple comparisons. Posterior computation proceeds via an efficient MCMC algorithm using the exact block Gibbs sampler. The methods are assessed using simulation studies and applied to a yeast cell-cycle gene expression data set.

Full Text

Duke Authors

Cited Authors

  • Hua, Z; Zhu, H; Dunson, DB

Published Date

  • May 2015

Published In

Volume / Issue

  • 7 / 1

Start / End Page

  • 90 - 107

PubMed ID

  • 26085848

Pubmed Central ID

  • 26085848

Electronic International Standard Serial Number (EISSN)

  • 1867-1772

International Standard Serial Number (ISSN)

  • 1867-1764

Digital Object Identifier (DOI)

  • 10.1007/s12561-013-9104-y

Language

  • eng