Biostatistics Seminar Series: Paul Rathouz, PhD

Title: Semiparametric Generalized Linear Models: Small, Large, and Biased SamplesAbstract: Rathouz and Gao (2009) proposed a novel class of generalized linear models indexed by a linear predictor and a link function for the mean of (Y|X). In this class, the distribution of (Y|X) is left unspecified and estimated from the data via exponential tilting of a reference distribution, yielding a response model that is a member of the natural exponential family. Originally, asymptotic results were developed for a response distribution with finite support under the framework of regular maximum likelihood estimation. Allowing support to be either finite or infinite (as will arise with continuous Y), in this talk, we present more recent results on inferences under small sample sizes, on asymptotics under infinite support, and on scalable computational methods as n->infinity. We also show how, with very easy-to-implement modifications, the model can accommodate biased samples arising from extensions of case-control designs to continuous response distributions.