Local polynomial kernel regression for generalized linear models and quasi-likelihood functions

Jianqing Fan, Nancy E. Heckman, M. P. Wand

Research output: Contribution to journalArticle

226 Scopus citations

Abstract

We investigate the extension of the nonparametric regression technique of local polynomial fitting with a kernel weight to generalized linear models and quasi-likelihood contexts. In the ordinary regression case, local polynomial fitting has been seen to have 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 carry over to generalized linear model and quasi-likelihood settings. We also derive the asymptotic distributions of the proposed class of estimators that allow for straightforward interpretation and extensions of state-of-the-art bandwidth selection methods.

Original languageEnglish (US)
Pages (from-to)141-150
Number of pages10
JournalJournal of the American Statistical Association
Volume90
Issue number429
DOIs
StatePublished - Mar 1995
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Bandwidth
  • Boundary effects
  • Local likelihood
  • Logistic regression
  • Nonparametric regression
  • Poisson regression

Fingerprint Dive into the research topics of 'Local polynomial kernel regression for generalized linear models and quasi-likelihood functions'. Together they form a unique fingerprint.

  • Cite this