A central limit theorem for the integrated square error of the kernel density estimators with randomly censored data
type: Item
instance of: scholarly article
title: A central limit theorem for the integrated square error of the kernel density estimators with randomly censored data
zbMATH Open document ID: 0799.62052
DOI: 10.1016/0378-3758(93)90083-I
author: Song Yang
published in: Journal of Statistical Planning and Inference
publication date: 1994-02-16T00:00:00Z
review text: To estimate the density in a random censorship model a kernel estimator is used where instead of the empirical distribution function the Kaplan- Meier estimator is applied. The integrated square error is used to describe the accuracy of the density estimator. To investigate the asymptotic behaviour of this error a central limit theorem is established which can be applied to construct goodness of fit tests and to the Hellinger distance parametric inference.
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 60F05
zbMATH DE Number: 502949
zbMATH Keywords: product-limit estimator
zbMATH Keywords: random censorship model
zbMATH Keywords: kernel estimator
zbMATH Keywords: Kaplan-Meier estimator
zbMATH Keywords: integrated square error
zbMATH Keywords: accuracy
zbMATH Keywords: central limit theorem
zbMATH Keywords: goodness of fit tests
zbMATH Keywords: Hellinger distance parametric inference
MaRDI profile type: Publication
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Wikidata QID: Q126407814
full work available at URL: 0378-3758(93)90083-i
OpenAlex ID: W1970620425
description: scientific article; zbMATH DE number 502949