Non-parametric hazard function estimation using the Kaplan-Meier estimator

Choongrak Kim; Whasoo Bae; Hyemi Choi; Byeong Park

doi:10.1080/10485250500337138

Non-parametric hazard function estimation using the Kaplan-Meier estimator

Choongrak Kim^*
, Whasoo Bae
, Hyemi Choi
, Byeong Park

^*Corresponding author for this work

Department of Statistics

Pusan National University
Inje University
Korea Advanced Institute of Science and Technology
Seoul National University

Research output: Contribution to journal › Journal article › peer-review

15 Scopus citations

Abstract

Estimation of the hazard function when the data are censored is an important problem in medical research. In this article, we propose a simple non-parametric estimator of the hazard function. Its asymptotic properties are derived, and numerical comparisons with other existing estimators are made. The proposed estimator is shown to be at least as good as the other estimators from both the theoretical and the numerical points of view.

Original language	English
Pages (from-to)	937-948
Number of pages	12
Journal	Journal of Nonparametric Statistics
Volume	17
Issue number	8
DOIs	https://doi.org/10.1080/10485250500337138
State	Published - 2005.12

Keywords

Bandwidth
Kaplan-Meier estimator
Kernel smoothing

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10.1080/10485250500337138

Cite this

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title = "Non-parametric hazard function estimation using the Kaplan-Meier estimator",

abstract = "Estimation of the hazard function when the data are censored is an important problem in medical research. In this article, we propose a simple non-parametric estimator of the hazard function. Its asymptotic properties are derived, and numerical comparisons with other existing estimators are made. The proposed estimator is shown to be at least as good as the other estimators from both the theoretical and the numerical points of view.",

keywords = "Bandwidth, Kaplan-Meier estimator, Kernel smoothing",

author = "Choongrak Kim and Whasoo Bae and Hyemi Choi and Byeong Park",

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AU - Kim, Choongrak

AU - Bae, Whasoo

AU - Choi, Hyemi

AU - Park, Byeong

PY - 2005/12

Y1 - 2005/12

N2 - Estimation of the hazard function when the data are censored is an important problem in medical research. In this article, we propose a simple non-parametric estimator of the hazard function. Its asymptotic properties are derived, and numerical comparisons with other existing estimators are made. The proposed estimator is shown to be at least as good as the other estimators from both the theoretical and the numerical points of view.

AB - Estimation of the hazard function when the data are censored is an important problem in medical research. In this article, we propose a simple non-parametric estimator of the hazard function. Its asymptotic properties are derived, and numerical comparisons with other existing estimators are made. The proposed estimator is shown to be at least as good as the other estimators from both the theoretical and the numerical points of view.

KW - Bandwidth

KW - Kaplan-Meier estimator

KW - Kernel smoothing

UR - https://www.scopus.com/pages/publications/30344489032

U2 - 10.1080/10485250500337138

DO - 10.1080/10485250500337138

M3 - Journal article

AN - SCOPUS:30344489032

SN - 1048-5252

VL - 17

SP - 937

EP - 948

JO - Journal of Nonparametric Statistics

JF - Journal of Nonparametric Statistics

IS - 8

ER -

Non-parametric hazard function estimation using the Kaplan-Meier estimator

Abstract

Keywords

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