Boosting local quasi-likelihood estimators

Masao Ueki, Kaoru Fueda

Research output: Contribution to journalArticlepeer-review

Abstract

For likelihood-based regression contexts, including generalized linear models, this paper presents a boosting algorithm for local constant quasi-likelihood estimators. Its advantages are the following: (a) the one-boosted estimator reduces bias in local constant quasi-likelihood estimators without increasing the order of the variance, (b) the boosting algorithm requires only one-dimensional maximization at each boosting step and (c) the resulting estimators can be written explicitly and simply in some practical cases.

Original languageEnglish
Pages (from-to)235-248
Number of pages14
JournalAnnals of the Institute of Statistical Mathematics
Volume62
Issue number2
DOIs
Publication statusPublished - Apr 1 2010

Keywords

  • Bias reduction
  • Generalized linear models
  • Kernel regression
  • L Boosting
  • Local quasi-likelihood
  • Nadaraya - Watson estimator

ASJC Scopus subject areas

  • Statistics and Probability

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