Generalized semiparametric odds ratio models

University Of Illinois At Chicago, Chicago IL

Investigators

Abstract

The investigator has two objectives to accomplish in this research project. The first objective is to develop theory of statistical inference for a new class of semi-parametric odds ratio models that include both the generalized linear model and the Cox regression model as special cases. The second objective is to apply this class of models to solve a number of theoretical problems that are of importance in applications. These applications include (1) addressing issues in parameter identifiability, estimation, and inference in biased sampling designs in studying the association of a disease with gene and environment factors, (2) introducing a new approach for testing goodness of fit of generalized linear models, and (3) developing a new flexible semi-parametric procedure for multivariate density estimation and survival analysis with complex dependence structures. Flexible and easily interpretable stochastic models are very useful in extracting information from data with complex structures. Such data are often collected in studies exploring the causes of diseases and other socio-economic problems. The investigator proposes a new class of models for the statistical analysis of such data. Results from this research will have broad applications in a variety of scientific fields. When applied to epidemiological studies, results from this research will be able to facilitate the design of more powerful statistical tests for detecting genetic and environmental causes of disease. When applied to sociological studies, results from this research will be able to facilitate the understanding of underlying causes of complex socio-economic problems so that better solutions to the problems can be found.