A kernel estimator of a density function in multivariate case from randomly censored data

Yong Zhou

doi:10.1016/s0252-9602(17)30792-0

A kernel estimator of a density function in multivariate case from randomly censored data

Yong Zhou^*

^*Corresponding author for this work

CAS - Institute of Applied Mathematics

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

Abstract

A kernel density estimator is proposed when the data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied.

Original language	English
Pages (from-to)	170-180
Number of pages	11
Journal	Acta Mathematica Scientia
Volume	16
Issue number	2
DOIs	https://doi.org/10.1016/s0252-9602(17)30792-0
State	Published - Apr 1996
Externally published	Yes

Keywords

Asymptotic normality
Kernel density estimator
Mean square error and censored data
Product-limit estimator

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10.1016/s0252-9602(17)30792-0

Cite this

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abstract = "A kernel density estimator is proposed when the data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied.",

keywords = "Asymptotic normality, Kernel density estimator, Mean square error and censored data, Product-limit estimator",

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KW - Kernel density estimator

KW - Mean square error and censored data

KW - Product-limit estimator

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A kernel estimator of a density function in multivariate case from randomly censored data

Abstract

Keywords

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