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Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields

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  • Michel Harel
  • Jean-François Lenain
  • Joseph Ngatchou-Wandji

Abstract

We investigate the asymptotic behaviour of binned kernel density estimators for dependent and locally non-stationary random fields converging to stationary random fields. We focus on the study of the bias and the asymptotic normality of the estimators. A simulation experiment conducted shows that both the kernel density estimator and the binned kernel density estimator have the same behavior and both estimate accurately the true density when the number of fields increases. We apply our results to the 2002 incidence rates of tuberculosis in the departments of France.

Suggested Citation

  • Michel Harel & Jean-François Lenain & Joseph Ngatchou-Wandji, 2016. "Asymptotic behaviour of binned kernel density estimators for locally non-stationary random fields," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 296-321, June.
    • Handle: RePEc:taf:gnstxx:v:28:y:2016:i:2:p:296-321
    DOI: 10.1080/10485252.2016.1163351
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    References listed on IDEAS

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    Cited by:

    1. Zhenyu Jiang & Nengxiang Ling & Zudi Lu & Dag Tj⊘stheim & Qiang Zhang, 2020. "On bandwidth choice for spatial data density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 817-840, July.

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