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Bayesian partial linear model for skewed longitudinal data.
Tang, Yuanyuan; Sinha, Debajyoti; Pati, Debdeep; Lipsitz, Stuart; Lipshultz, Steven.
Afiliação
  • Tang Y; Department of Statistics, Florida State University, Tallahassee, FL, USA.
  • Sinha D; Department of Statistics, Florida State University, Tallahassee, FL, USA.
  • Pati D; Department of Statistics, Florida State University, Tallahassee, FL, USA debdeep@stat.fsu.edu.
  • Lipsitz S; Brigham and Women's Hospital, Boston, MA, USA.
  • Lipshultz S; Department of Pediatrics, Wayne State University School of Medicine and Children's Hospital of Michigan, Detroit, MI, USA.
Biostatistics ; 16(3): 441-53, 2015 Jul.
Article em En | MEDLINE | ID: mdl-25792623
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Lineares / Teorema de Bayes Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Child / Child, preschool / Female / Humans / Infant / Newborn / Pregnancy Idioma: En Revista: Biostatistics Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Lineares / Teorema de Bayes Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Child / Child, preschool / Female / Humans / Infant / Newborn / Pregnancy Idioma: En Revista: Biostatistics Ano de publicação: 2015 Tipo de documento: Article País de afiliação: Estados Unidos