Nonparametric tilted density function estimation: a cross-validation criterion

Hassan Doosti*, Peter Hall, Jorge Mateu

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    In this paper, we propose a tilted estimator for nonparametric estimation of a density function. We use a cross-validation criterion to choose both the bandwidth and the tilted estimator parameters. We demonstrate theoretically that our proposed estimator provides a convergence rate which is strictly faster than the usual rate attained using a conventional kernel estimator with a positive kernel. We investigate the performance through both theoretical and numerical studies.

    Original languageEnglish
    Pages (from-to)51-68
    Number of pages18
    JournalJournal of Statistical Planning and Inference
    Publication statusPublished - Dec 2018


    • Cross validation function
    • Non-parametric density function estimation
    • Rate of convergence
    • Tilted estimators


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