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A note on the performance of bootstrap kernel density estimation with small re-sample sizes

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  • Mojirsheibani, Majid

Abstract

This paper studies the unconditional limiting distribution of the maximal deviation of bootstrap kernel density estimators with re-sample sizes that are different from the sample size, n. More specifically, we study the convergence rates of such statistics when the bootstrap sample size may be orders of magnitude smaller than n. An application to big-data scenarios is given.

Suggested Citation

  • Mojirsheibani, Majid, 2021. "A note on the performance of bootstrap kernel density estimation with small re-sample sizes," Statistics & Probability Letters, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:stapro:v:178:y:2021:i:c:s0167715221001516
    DOI: 10.1016/j.spl.2021.109189
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    References listed on IDEAS

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    1. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.
    2. Mojirsheibani, Majid, 2012. "A weighted bootstrap approximation of the maximal deviation of kernel density estimates over general compact sets," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 230-241.
    3. Konakov, V. D. & Piterbarg, V. I., 1984. "On the convergence rate of maximal deviation distribution for kernel regression estimates," Journal of Multivariate Analysis, Elsevier, vol. 15(3), pages 279-294, December.
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