Hazard function estimation from homogeneous right censored data with missing censoring indicators

Sundarraman Subramanian; Derek Bean

doi:10.1016/j.stamet.2008.01.003

Hazard function estimation from homogeneous right censored data with missing censoring indicators

Sundarraman Subramanian, Derek Bean

Mathematical Sciences

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

The kernel smoothed Nelson-Aalen estimator has been well investigated, but is unsuitable when some of the censoring indicators are missing. A representation introduced by Dikta, however, facilitates hazard estimation when there are missing censoring indicators. In this article, we investigate (i) a kernel smoothed semiparametric hazard estimator and (ii) a kernel smoothed "pre-smoothed" Nelson-Aalen estimator. We derive the asymptotic normality of the proposed estimators and compare their asymptotic variances.

Original language	English (US)
Pages (from-to)	515-527
Number of pages	13
Journal	Statistical Methodology
Volume	5
Issue number	6
DOIs	https://doi.org/10.1016/j.stamet.2008.01.003
State	Published - Nov 2008

All Science Journal Classification (ASJC) codes

Statistics and Probability

Keywords

Bandwidth
Empirical estimators
Liapounov central limit theorem
Maximum likelihood estimator
Missing at random
Semiparametric random censorship models

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10.1016/j.stamet.2008.01.003

Cite this

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title = "Hazard function estimation from homogeneous right censored data with missing censoring indicators",

abstract = "The kernel smoothed Nelson-Aalen estimator has been well investigated, but is unsuitable when some of the censoring indicators are missing. A representation introduced by Dikta, however, facilitates hazard estimation when there are missing censoring indicators. In this article, we investigate (i) a kernel smoothed semiparametric hazard estimator and (ii) a kernel smoothed {"}pre-smoothed{"} Nelson-Aalen estimator. We derive the asymptotic normality of the proposed estimators and compare their asymptotic variances.",

keywords = "Bandwidth, Empirical estimators, Liapounov central limit theorem, Maximum likelihood estimator, Missing at random, Semiparametric random censorship models",

author = "Sundarraman Subramanian and Derek Bean",

note = "Funding Information: The authors thank the Editor-in-Chief, an Associate Editor, and the reviewers for their timely reviews and useful feedback. First author{\textquoteright}s research was partly supported by the U.S. National Cancer Institute grant R15 CA103845. Second author{\textquoteright}s research was partly supported by the University of Maine. ",

year = "2008",

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doi = "10.1016/j.stamet.2008.01.003",

language = "English (US)",

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journal = "Statistical Methodology",

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T1 - Hazard function estimation from homogeneous right censored data with missing censoring indicators

AU - Subramanian, Sundarraman

AU - Bean, Derek

N1 - Funding Information: The authors thank the Editor-in-Chief, an Associate Editor, and the reviewers for their timely reviews and useful feedback. First author’s research was partly supported by the U.S. National Cancer Institute grant R15 CA103845. Second author’s research was partly supported by the University of Maine.

PY - 2008/11

Y1 - 2008/11

N2 - The kernel smoothed Nelson-Aalen estimator has been well investigated, but is unsuitable when some of the censoring indicators are missing. A representation introduced by Dikta, however, facilitates hazard estimation when there are missing censoring indicators. In this article, we investigate (i) a kernel smoothed semiparametric hazard estimator and (ii) a kernel smoothed "pre-smoothed" Nelson-Aalen estimator. We derive the asymptotic normality of the proposed estimators and compare their asymptotic variances.

AB - The kernel smoothed Nelson-Aalen estimator has been well investigated, but is unsuitable when some of the censoring indicators are missing. A representation introduced by Dikta, however, facilitates hazard estimation when there are missing censoring indicators. In this article, we investigate (i) a kernel smoothed semiparametric hazard estimator and (ii) a kernel smoothed "pre-smoothed" Nelson-Aalen estimator. We derive the asymptotic normality of the proposed estimators and compare their asymptotic variances.

KW - Bandwidth

KW - Empirical estimators

KW - Liapounov central limit theorem

KW - Maximum likelihood estimator

KW - Missing at random

KW - Semiparametric random censorship models

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DO - 10.1016/j.stamet.2008.01.003

M3 - Article

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SN - 1572-3127

VL - 5

SP - 515

EP - 527

JO - Statistical Methodology

JF - Statistical Methodology

IS - 6

ER -

Hazard function estimation from homogeneous right censored data with missing censoring indicators

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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