Bootstrap for the conditional distribution function with truncated and censored data

Autor:	W. González Manteiga, M. C. Iglesias Pérez
Rok vydání:	2003
Předmět:	Statistics and Probability Statistics::Theory Convergence of random variables Consistency (statistics) Resampling Kernel density estimation Statistics Statistics::Methodology Estimator Asymptotic distribution Truncation (statistics) Conditional probability distribution Mathematics
Zdroj:	Annals of the Institute of Statistical Mathematics. 55:331-357
ISSN:	1572-9052 0020-3157
DOI:	10.1007/bf02530503
Popis:	We propose a resampling method for left truncated and right censored data with covariables to obtain a bootstrap version of the conditional distribution function estimator. We derive an almost sure representation for this bootstrapped estimator and, as a consequence, the consistency of the bootstrap is obtained. This bootstrap approximation represents an alternative to the normal asymptotic distribution and avoids the estimation of the complicated mean and variance parameters of the latter.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::62c4090bbce0661380205fb42d23bce0 https://doi.org/10.1007/bf02530503 Zobrazit plný text záznamu Full text from SpringerLink