LOCAL POLYNOMIAL KERNEL REGRESSION FOR GENERALIZED LINEAR-MODELS AND QUASI-LIKELIHOOD FUNCTIONS

We investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to generalized linea r models and quasi-likelihood contexts. In the ordinary regression cas e, local polynomial fitting has been seen to have several appealing fe atures in terms of intuitive and mathematical simplicity. One notewort hy feature is the better performance near the boundaries compared to t he traditional kernel regression estimators. These properties are show n to carry over to generalized linear model and quasi-likelihood setti ngs. We also derive the asymptotic distributions of the proposed class of estimators that allow for straightforward interpretation and exten sions of state-of-the-art bandwidth selection methods.