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Asymptotic Properties of Hazard Rate Estimator in Censored Linear Regression

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  • Fuxia Cheng

    (Illinois State University)

Abstract

We consider nonparametric inference for error hazard rates in linear regression with right censored data. The estimator for hazard rate function is defined based on the kernel-smoothed estimator of error density, which makes use of the Kaplan-Meier estimator of the error distribution. We obtain the limit distribution for the maximum deviation of the hazard rate estimator.

Suggested Citation

  • Fuxia Cheng, 2017. "Asymptotic Properties of Hazard Rate Estimator in Censored Linear Regression," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(1), pages 1-12, February.
    • Handle: RePEc:spr:sankha:v:79:y:2017:i:1:d:10.1007_s13171-016-0095-x
    DOI: 10.1007/s13171-016-0095-x
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    References listed on IDEAS

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    1. Cheng, Fuxia, 2012. "Maximum deviation of error density estimators in censored linear regression," Statistics & Probability Letters, Elsevier, vol. 82(9), pages 1657-1664.
    2. Diehl, Sabine & Stute, Winfried, 1988. "Kernel density and hazard function estimation in the presence of censoring," Journal of Multivariate Analysis, Elsevier, vol. 25(2), pages 299-310, May.
    3. Ingrid Van Keilegom & Noël Veraverbeke, 2001. "Hazard Rate Estimation in Nonparametric Regression with Censored Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(4), pages 730-745, December.
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