Wednesday, November 4, 2009
04:30 PM in REC 315
Assistant Professor Guang Cheng
Department of Statistics, Purdue University

On Semiparametric Inference

Abstract

Semiparametric modelling is an excellent framework due to its flexibility to model some features parametrically without making assumptions on the other features. The infinite-dimensional nuisance parameter in the semiparametric models generally poses several challenges for making maximum likelihood inference for the parameter of interest at both theoretical and methodological levels. In this talk, in order to avoid those challenges, I first talk about several different ways to do semiparametric inference, i.e. MCMC, Bootstrap and Numerical methods, and their higher order theoretical properties. Next I will focus on Some special interesting topics, i.e. variable selection and isotonic regression, in the semiparametric models. The related future research directions are also mentioned.