Prof. Douglas G. Simpson : Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models

Posted by , part of the Statistics and Data Science Seminar.

At

Oct. 24, 2007, 3:30 p.m.

In

SEO 712

Abstract

A class of heteroscedastic generalized linear regression models is developed in which a subset of the regression parameters are scaled nonparametrically. Efficient semiparametric inferences are derived for the parametric components of the models. Bootstrap tests for scale heterogenerity are also developed. The models provide an approach to adapt for heterogeneity in the data due factors such as to varying exposures and varying levels of aggregation. The methodology is illustrated with simulations, published data and data from collaborative research on ultrasound safety.