Practical Semiparametric Bayes Analysis of Heteroscedastic and Skewed Response

• Activity Number: 294
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Yuanyuan Tang*+ and Debdeep Pati and Debajyoti Sinha
• Companies: AbbVie and Florida State University and Florida State University
• Keywords: Bayesian ; Skewed ; Heteroscedastic ; Semiparametric ; Biostatistics ; nonparametric

Abstract

In biomedical studies, the covariates often affect the location and scale, as well as the shape of the skewed response distribution. Existing biostatistics literature mainly focuses on the mean regression with a symmetric error distribution. While such modeling assumptions and methods are often deemed as restrictive and inappropriate for skewed response, the completely nonparametric methods may lack a physical interpretation of the covariate effects. Existing nonparametric methods also miss any easily implementable computational tool. For a skewed response, we develop a novel model accommodating a nonparametric error density that depends on the covariates. The advantages of our semiparametric associated Bayes method include the ease of prior elicitation/determination, an easily implementable posterior computation, theoretically sound properties of the selection of priors and accommodation of possible outliers. The practical advantages of the method are illustrated via a simulation study and an analysis of a real-life epidemiological study on the serum response to DDT exposure during gestation period