title: Bayesian partial linear model for skewed longitudinal data.
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.
authors: Department of Statistics, Florida State University, Tallahassee, FL, USA.
authors: Yuanyuan
authors: Y
authors: Tang
authors: Department of Statistics, Florida State University, Tallahassee, FL, USA.
authors: Debajyoti
authors: D
authors: Sinha
authors: Department of Statistics, Florida State University, Tallahassee, FL, USA debdeep@stat.fsu.edu.
authors: Debdeep
authors: D
authors: Pati
authors: Brigham and Women's Hospital, Boston, MA, USA.
authors: Stuart
authors: S
authors: Lipsitz
authors: Department of Pediatrics, Wayne State University School of Medicine and Children's Hospital of Michigan, Detroit, MI, USA.
authors: Steven
authors: S
authors: Lipshultz
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D005260:Female; D015658:HIV Infections / Q000150:complications / Q000635:transmission; D006330:Heart Defects, Congenital / Q000209:etiology; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D018445:Infectious Disease Transmission, Vertical; D016014:Linear Models; D008137:Longitudinal Studies; D011247:Pregnancy; D011251:Pregnancy Complications, Infectious
keywords: Dirichlet process; Median regression; Partial linear model; Semiparametric; Skewed error