Optimal tuning parameter estimation in maximum penalized likelihood method

Masao Ueki, Kaoru Fueda

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)

    Abstract

    In maximum penalized or regularized methods, it is important to select a tuning parameter appropriately. This paper proposes a direct plug-in method for tuning parameter selection. The tuning parameters selected using a generalized information criterion (Konishi and Kitagawa, Biometrika, 83, 875-890, 1996) and cross-validation (Stone, Journal of the Royal Statistical Society, Series B, 58, 267-288, 1974) are shown to be asymptotically equivalent to those selected using the proposed method, from the perspective of estimation of an optimal tuning parameter. Because of its directness, the proposed method is superior to the two selection methods mentioned above in terms of computational cost. Some numerical examples which contain the penalized spline generalized linear model regressions are provided.

    Original languageEnglish
    Pages (from-to)413-438
    Number of pages26
    JournalAnnals of the Institute of Statistical Mathematics
    Volume62
    Issue number3
    DOIs
    Publication statusPublished - 2010 Jun 1

    Keywords

    • Cross-validation
    • Direct plug-in method
    • Generalized information criterion
    • Kullback-leibler information
    • Maximum penalized likelihood method
    • Penalized spline
    • Ridge regression
    • Tuning parameter estimation

    ASJC Scopus subject areas

    • Statistics and Probability

    Fingerprint Dive into the research topics of 'Optimal tuning parameter estimation in maximum penalized likelihood method'. Together they form a unique fingerprint.

    Cite this