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A weighted estimator of conditional hazard rate with left-truncated and dependent data

Author

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  • Han-Ying Liang

    (Tongji University)

  • Elias Ould Saïd

    (Univ. Littoral Côte d’Opale, LMPA)

Abstract

Based on empirical likelihood method, we construct new weighted estimators of conditional density and conditional survival functions when the interest random variable is subject to random left-truncation; further, we define a plug-in weighted estimator of the conditional hazard rate. Under strong mixing assumptions, we derive asymptotic normality of the proposed estimators which permit to built a confidence interval for the conditional hazard rate. The finite sample behavior of the estimators is investigated via simulations too.

Suggested Citation

  • Han-Ying Liang & Elias Ould Saïd, 2018. "A weighted estimator of conditional hazard rate with left-truncated and dependent data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 155-189, February.
    • Handle: RePEc:spr:aistmt:v:70:y:2018:i:1:d:10.1007_s10463-016-0587-4
    DOI: 10.1007/s10463-016-0587-4
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    References listed on IDEAS

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    1. Xiong, Xianzhu & Ou, Meijuan & Chen, Ailian, 2021. "Reweighted Nadaraya–Watson estimation of conditional density function in the right-censored model," Statistics & Probability Letters, Elsevier, vol. 168(C).

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