One-step local quasi-likelihood estimation

Jianqing Fan, Jianwei Chen

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

Local quasi-likelihood estimation is a useful extension of local least squares methods, but its computational cost and algorithmic convergence problems make the procedure less appealing, particularly when it is iteratively used in methods such as the back-fitting algorithm, cross-validation and bootstrapping. A one-step local quasi-likelihood estimator is introduced to overcome the computational drawbacks of the local quasi-likelihood method. We demonstrate that as long as the initial estimators are reasonably good the one-step estimator has the same asymptotic behaviour as the local quasi-likelihood method. Our simulation shows that the one-step estimator performs at least as well as the local quasi-likelihood method for a wide range of choices of bandwidths. A data-driven bandwidth selector is proposed for the one-step estimator based on the pre-asymptotic substitution method of Fan and Gijbels. It is then demonstrated that the data-driven one-step local quasi-likelihood estimator performs as well as the maximum local quasi-likelihood estimator by using the ideal optimal bandwidth.

Original languageEnglish (US)
Pages (from-to)927-943
Number of pages17
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume61
Issue number4
DOIs
StatePublished - 1999

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Bandwidth selection
  • Generalized linear models
  • Nonparametric regression
  • One-step estimation
  • Quasi-likelihood

Fingerprint Dive into the research topics of 'One-step local quasi-likelihood estimation'. Together they form a unique fingerprint.

Cite this