On Gauss quadrature and partial cross validation

A. S. Kozek*, J. Yin

*Corresponding author for this work

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    New estimators of expected values Ew(X) of functions of a random variable X are introduced. The new estimators are based on Gauss quadrature, a numerical method frequently used to approximate integrals over finite intervals. The estimators need a small number of numerical evaluations and hence are useful in partial cross validation (PCV) a numerical method for finding optimal smoothing parameters in nonparametric curve estimation. The PCV can considerably reduce the computational cost of the generalized cross validation method typically used to determine the optimal smoothing parameter.

    Original languageEnglish
    Pages (from-to)431-448
    Number of pages18
    JournalComputational Statistics and Data Analysis
    Volume45
    Issue number3
    DOIs
    Publication statusPublished - 10 Apr 2004

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