Boosting local quasi-likelihood estimators

Masao Ueki, Kaoru Fueda

    Research output: Contribution to journalArticlepeer-review


    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
    Issue number2
    Publication statusPublished - 2010 Apr


    • 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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