Maximum deviation of error density estimators in censored linear regression
AbstractThis paper considers the maximum deviation of the error density estimator in linear regression with right censored data. Based on the Kaplan–Meier estimator of the residual distribution, we define the kernel-smoothed estimator of an error density function. The limit distributions for the maximum deviation of the estimator are obtained.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 82 (2012)
Issue (Month): 9 ()
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Diehl, Sabine & Stute, Winfried, 1988. "Kernel density and hazard function estimation in the presence of censoring," Journal of Multivariate Analysis, Elsevier, vol. 25(2), pages 299-310, May.
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