Statistics Seminar: Thomas Fung (Macquarie University) -- Semiparametric generalized linear models for time-series data

Abstract:

We introduce a semiparametric generalized linear models framework for time-series 
data that does not require specification of a working distribution or variance 
function for the data. Rather, the conditional response distribution is treated 
as an infinite-dimensional parameter, which is then estimated simultaneously with 
the usual finite-dimensional parameters via a maximum empirical likelihood 
approach. A general consistency result for the resulting estimators is shown. 
Simulations suggest that both estimation and inferences using the proposed method 
can perform as well as a correctly-specified parametric model even for moderate 
sample sizes, but is much more robust than parametric methods under model 
misspecification. The method is used to analyse the Polio dataset from Zeger 
(1988). This talk represents joint research with Dr Alan Huang.