Bayesian partial linear model for skewed longitudinal data

Yuanyuan Tang, Debajyoti Sinha, Debdeep Pati, Stuart Lipsitz, Steven E Lipshultz

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Unlike majority of current statistical models and methods focusing on mean response for highly skewed longitudinal data, we present a novel model for such data accommodating a partially linear median regression function, a skewed error distribution and within subject association structures. We provide theoretical justifications for our methods including asymptotic properties of the posterior and associated semiparametric Bayesian estimators. We also provide simulation studies to investigate the finite sample properties of our methods. Several advantages of our method compared with existing methods are demonstrated via analysis of a cardiotoxicity study of children of HIV-infected mothers.

Original languageEnglish (US)
Pages (from-to)441-453
Number of pages13
JournalBiostatistics
Volume16
Issue number3
DOIs
StatePublished - Sep 17 2014
Externally publishedYes

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Keywords

  • Dirichlet process
  • Median regression
  • Partial linear model
  • Semiparametric
  • Skewed error.

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Tang, Y., Sinha, D., Pati, D., Lipsitz, S., & Lipshultz, S. E. (2014). Bayesian partial linear model for skewed longitudinal data. Biostatistics, 16(3), 441-453. https://doi.org/10.1093/biostatistics/kxv005