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Strong representations of the Kaplan–Meier estimator and hazard estimator with censored widely orthant dependent data

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Listed:
  • Yi Wu

    (Anhui University)

  • Wei Yu

    (Anhui University)

  • Xuejun Wang

    (Anhui University)

Abstract

In this paper, we investigate the rates of strong consistency and the strong representations for the Kaplan–Meier estimator and hazard estimator with censored widely orthant dependent data. Under some mild conditions, the rates of strong consistency are shown to be $$O(n^{-1/2}[\ln (ng(n))]^{1/2})~a.s.$$ O ( n - 1 / 2 [ ln ( n g ( n ) ) ] 1 / 2 ) a . s . , where g(n) are the dominating coefficients of widely orthant dependent random variables. Under the same conditions, the strong representations of the two estimators are also obtained with the remainder of order $$O(n^{-1/2}[\ln (ng(n))]^{1/2})~a.s.$$ O ( n - 1 / 2 [ ln ( n g ( n ) ) ] 1 / 2 ) a . s . As an application, the results are generalized to Farlie-Gumbel-Morgenstern sequences. These results extend the corresponding ones for independent and some dependent data. Some numerical simulations and a real example analysis are also presented to confirm the theoretical results.

Suggested Citation

  • Yi Wu & Wei Yu & Xuejun Wang, 2022. "Strong representations of the Kaplan–Meier estimator and hazard estimator with censored widely orthant dependent data," Computational Statistics, Springer, vol. 37(1), pages 383-402, March.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01125-z
    DOI: 10.1007/s00180-021-01125-z
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    References listed on IDEAS

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