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Smoothed stationary bootstrap bandwidth selection for density estimation with dependent data

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  • Barbeito, Inés
  • Cao, Ricardo

Abstract

A smoothed version of the stationary bootstrap is established for the purpose of bandwidth selection in density estimation for dependent data. An exact expression for the bootstrap version of the mean integrated squared error under dependence is obtained in this context. This is very useful since implementation of the bootstrap selector does not require Monte Carlo approximation. A simulation study is carried out to show the good practical performance of the new bootstrap bandwidth selector with respect to other existing competitors. The method is illustrated by applying it to two real data sets.

Suggested Citation

  • Barbeito, Inés & Cao, Ricardo, 2016. "Smoothed stationary bootstrap bandwidth selection for density estimation with dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 130-147.
    • Handle: RePEc:eee:csdana:v:104:y:2016:i:c:p:130-147
    DOI: 10.1016/j.csda.2016.06.015
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    References listed on IDEAS

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    1. Cao, R., 1993. "Bootstrapping the Mean Integrated Squared Error," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 137-160, April.
    2. Ricardo Cao, 1999. "An overview of bootstrap methods for estimating and predicting in time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(1), pages 95-116, June.
    3. Hwang, Eunju & Shin, Dong Wan, 2012. "Stationary bootstrap for kernel density estimators under ψ-weak dependence," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1581-1593.
    4. Kim, Tae Yoon & Cox, Denis D., 1997. "A Study on Bandwidth Selection in Density Estimation under Dependence," Journal of Multivariate Analysis, Elsevier, vol. 62(2), pages 190-203, August.
    5. Feluch, W. & Koronacki, J., 1992. "A note on modified cross-validation in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 13(2), pages 143-151, March.
    6. Cao, Ricardo & Cuevas, Antonio & Gonzalez Manteiga, Wensceslao, 1994. "A comparative study of several smoothing methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 153-176, February.
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    Cited by:

    1. Ricardo Cao, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 664-670, September.

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