Statistics and Data Science Seminar
Prof. Douglas G. Simpson
University of Illinois at Urbana-Champaign
Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models
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.
Wednesday October 24, 2007 at 3:30 PM in SEO 712