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Asymptotic Behavior of Bandwidth Selected by the Cross-Validation Method for Local Polynomial Fitting

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  • Xia, Yingcun
  • Li, W. K.

Abstract

In this paper, the asymptotic optimality of the cross validation bandwidth selector for the local polynomial fitting under strongly mixing dependence is obtained. The asymptotic normality of the bandwidth selected by the cross-validation method is derived, which is an extension of W. Härdle, P. Hall, and J. S. Marron (1988, J. Am. Statist. Assoc.83, 86-101).

Suggested Citation

  • Xia, Yingcun & Li, W. K., 2002. "Asymptotic Behavior of Bandwidth Selected by the Cross-Validation Method for Local Polynomial Fitting," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 265-287, November.
  • Handle: RePEc:eee:jmvana:v:83:y:2002:i:2:p:265-287
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    1. Wolfgang Härdle & Philippe Vieu, 1992. "Kernel Regression Smoothing Of Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(3), pages 209-232, May.
    2. Vieu, Philippe, 1991. "Quadratic errors for nonparametric estimates under dependence," Journal of Multivariate Analysis, Elsevier, vol. 39(2), pages 324-347, November.
    3. Kim, T. Y. & Cox, D. D., 1995. "Asymptotic Behaviors of Some Measures of Accuracy in Nonparametric Curve Estimation with Dependent Observations," Journal of Multivariate Analysis, Elsevier, vol. 53(1), pages 67-93, April.
    4. Tae Yoon Kim & Dennis D. Cox, 1996. "Bandwidth Selection In Kernel Smoothing Of Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(1), pages 49-63, January.
    5. Elias Masry, 1996. "Multivariate Local Polynomial Regression For Time Series:Uniform Strong Consistency And Rates," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(6), pages 571-599, November.
    6. Jones, M. C., 1991. "The roles of ISE and MISE in density estimation," Statistics & Probability Letters, Elsevier, vol. 12(1), pages 51-56, July.
    7. Hall, Peter, 1984. "Central limit theorem for integrated square error of multivariate nonparametric density estimators," Journal of Multivariate Analysis, Elsevier, vol. 14(1), pages 1-16, February.
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