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The uniformly strong consistency of kernel-type distribution estimator under asymptotically almost negatively associated samples

Author

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  • Shipeng Wu
  • Yi Wu
  • Wenzhi Yang
  • Xuejun Wang

Abstract

This paper studies the kernel-type distribution estimator based on asymptotically almost negatively associated (AANA, for short) samples. The rate of uniformly strong consistency is established under some mild conditions. As applications, the uniformly strong convergence rates of kernel-type density estimator and kernel-type hazard rate estimator are also obtained. Some Monte Carlo simulations are presented to illustrate the finite sample performance of the kernel method. Finally, a real data analysis of Alibaba stock returns data is used to illustrate the usefulness of the proposed methodology.

Suggested Citation

  • Shipeng Wu & Yi Wu & Wenzhi Yang & Xuejun Wang, 2025. "The uniformly strong consistency of kernel-type distribution estimator under asymptotically almost negatively associated samples," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 9(2), pages 124-140, April.
    • Handle: RePEc:taf:tstfxx:v:9:y:2025:i:2:p:124-140
    DOI: 10.1080/24754269.2025.2484980
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