Probability density estimation for survival data with censoring indicators missing at random

Qihua Wang; Wei Liu; Chunling Liu

doi:10.1016/j.jmva.2008.09.004

Probability density estimation for survival data with censoring indicators missing at random

Qihua Wang, Wei Liu, Chunling Liu

Research output: Journal article publication › Journal article › Academic research › peer-review

12 Citations (Scopus)

Abstract

In this paper, some nonparametric approaches of density function estimation are developed when censoring indicators are missing at random. A conditional mean score based estimator and a mean score estimator are suggested, respectively. The two estimators are proved to be asymptotically normal and uniformly strongly consistent. The bandwidth selection problem is also discussed. A simulation study is conducted to compare finite-sample behaviors of the proposed estimators.

Original language	English
Pages (from-to)	835-850
Number of pages	16
Journal	Journal of Multivariate Analysis
Volume	100
Issue number	5
DOIs	https://doi.org/10.1016/j.jmva.2008.09.004
Publication status	Published - 1 May 2009
Externally published	Yes

Keywords

62E20
62G05
Asymptotic normality
Bandwidth selection
Mean-squared error
Missing at random
primary
secondary
Strong consistency

ASJC Scopus subject areas

Statistics, Probability and Uncertainty
Numerical Analysis
Statistics and Probability

More information

10.1016/j.jmva.2008.09.004

Cite this

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abstract = "In this paper, some nonparametric approaches of density function estimation are developed when censoring indicators are missing at random. A conditional mean score based estimator and a mean score estimator are suggested, respectively. The two estimators are proved to be asymptotically normal and uniformly strongly consistent. The bandwidth selection problem is also discussed. A simulation study is conducted to compare finite-sample behaviors of the proposed estimators.",

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AU - Liu, Wei

AU - Liu, Chunling

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N2 - In this paper, some nonparametric approaches of density function estimation are developed when censoring indicators are missing at random. A conditional mean score based estimator and a mean score estimator are suggested, respectively. The two estimators are proved to be asymptotically normal and uniformly strongly consistent. The bandwidth selection problem is also discussed. A simulation study is conducted to compare finite-sample behaviors of the proposed estimators.

AB - In this paper, some nonparametric approaches of density function estimation are developed when censoring indicators are missing at random. A conditional mean score based estimator and a mean score estimator are suggested, respectively. The two estimators are proved to be asymptotically normal and uniformly strongly consistent. The bandwidth selection problem is also discussed. A simulation study is conducted to compare finite-sample behaviors of the proposed estimators.

KW - 62E20

KW - 62G05

KW - Asymptotic normality

KW - Bandwidth selection

KW - Mean-squared error

KW - Missing at random

KW - primary

KW - secondary

KW - Strong consistency

UR - https://www.scopus.com/pages/publications/60549096083

U2 - 10.1016/j.jmva.2008.09.004

DO - 10.1016/j.jmva.2008.09.004

M3 - Journal article

SN - 0047-259X

VL - 100

SP - 835

EP - 850

JO - Journal of Multivariate Analysis

JF - Journal of Multivariate Analysis

IS - 5

ER -

Probability density estimation for survival data with censoring indicators missing at random

Abstract

Keywords

ASJC Scopus subject areas

More information

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