Spring 2008 Thursday, February 7   Guang Cheng Postdoctoral Fellow SAMSI "Higher Order Semiparametric Inference Based on the Profile Likelihood" 3:00 Refreshments in 241 Schaeffer Hall 3:30 Talk in 140 Schaeffer Hall Semiparametric modelling provides a flexible framework to model some features parametrically without making assumptions about the others. However, 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. We will construct a series of profile likelihood based semiparametric inference procedures either based on numerical methods, i.e. K-step MLE, or through MCMC sampling, i.e. the Profile Sampler and the Penalized Profile Sampler. All the above profile likelihood based methods avoid evaluation of the infinite-dimensional operator and are easy to implement. Furthermore, we investigate their second order asymptotic behaviors, which are proven to be related to the convergence rate of the nuisance parameter and thus adjustable.

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