Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions

Generalized linear models (Wedderburn and NeIder 1972, McCullagh and NeIder 1988) were introduced as a means of extending the techniques of ordinary parametric regression to several commonly-used regression models arising from non-normal likelihoods. Typically these models have a variance that depends on the mean function. However, in many cases the likelihood is unknown, but the relationship between mean and variance can be specified. This has led to the consideration of quasi-likelihood methods, where the conditionallog-likelihood is replaced by a quasi-likelihood function. In this article we investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to these more general contexts. In the ordinary regression case local polynomial fitting has been seen to possess several appealing features in terms of intuitive and mathematical simplicity. One noteworthy feature is the better performance near the boundaries compared to the traditional kernel regression estimators. These properties are shown to carryover to the generalized linear model and quasi-likelihood model. The end result is a class of kernel type estimators for smoothing in quasi-likelihood models. These estimators can be viewed as a straightforward generalization of the usual parametric estimators. In addition, their simple asymptotic distributions allow for simple interpretation and extensions of state-of-the-art bandwidth selection methods.