Title | Semiparametric Bayes local additive models for longitudinal data. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Hua, Zhaowei, Hongtu Zhu, and David B. Dunson |
Journal | Stat Biosci |
Volume | 7 |
Issue | 1 |
Pagination | 90-107 |
Date Published | 2015 May 01 |
ISSN | 1867-1764 |
Abstract | 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. |
DOI | 10.1007/s12561-013-9104-y |
Alternate Journal | Stat Biosci |
Original Publication | Semiparametric Bayes local additive models for longitudinal data. |
PubMed ID | 26085848 |
PubMed Central ID | PMC4465815 |
Grant List | R01 MH086633 / MH / NIMH NIH HHS / United States R01 ES017240 / ES / NIEHS NIH HHS / United States UL1 RR025747 / RR / NCRR NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R21 AG033387 / AG / NIA NIH HHS / United States |
Semiparametric Bayes local additive models for longitudinal data.
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