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Asymptotic normality of some kernel-type estimators of probability density

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  • Bradley, Richard C.

Abstract

Central limit theorems are proved for some kernel-type estimators of probability density in the case where the observations form a strictly random sequence satisfying the [varrho]-mixing condition with a certain logarithmic mixing rate.

Suggested Citation

  • Bradley, Richard C., 1983. "Asymptotic normality of some kernel-type estimators of probability density," Statistics & Probability Letters, Elsevier, vol. 1(6), pages 295-300, October.
    • Handle: RePEc:eee:stapro:v:1:y:1983:i:6:p:295-300
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    Cited by:

    1. Liebscher, Eckhard, 1999. "Asymptotic normality of nonparametric estimators under [alpha]-mixing condition," Statistics & Probability Letters, Elsevier, vol. 43(3), pages 243-250, July.
    2. Müller, Ursula U. & Schick, Anton & Wefelmeyer, Wolfgang, 2015. "Estimators in step regression models," Statistics & Probability Letters, Elsevier, vol. 100(C), pages 124-129.
    3. Bosq, Denis & Merlevède, Florence & Peligrad, Magda, 1999. "Asymptotic Normality for Density Kernel Estimators in Discrete and Continuous Time," Journal of Multivariate Analysis, Elsevier, vol. 68(1), pages 78-95, January.

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