Maximum Likelihood Estimation for Multivariate Semiparametric Density Ratio Model

• Activity Number: 431
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Scott Marchese*
• Keywords: semiparametric ; density ratio ; robust ; multivariate

Abstract

The density ratio model postulates that the conditional density/mass function of the outcome variable is the product of a baseline density/mass function and a known parametric function containing the covariate information. This model has received increasing attention in recent years because of its natural connection with many commonly-used generalized linear models. In this paper, we propose a semiparametric density ratio model for multivariate outcome data. The proposed model allows for the analysis of covariate effects jointly on various types of data including continuous, binary, and count data. We develop efficient likelihood-based estimation and inference procedures and establish the large sample properties of the proposed nonparametric maximum likelihood estimators. Extensive simulation studies and an application to a real example are provided.

Authors: Scott Marchese, Guoqing Diao and Jing Qin