The central limit theorem under random truncation

Under left truncation, data (X(i), Y(i)) are observed only when Y(i) ≤ X(i). Usually, the distribution function F of the X(i) is the target of interest. In this paper, we study linear functionals ∫ φ dF(n) of the nonparametric maximum likelihood estimator (MLE) of F, the Lynden-Bell estimator F(n)....

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Published inBernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability Vol. 14; no. 3; p. 604
Main Authors Stute, Winfried, Wang, Jane-Ling
Format Journal Article
LanguageEnglish
Published England 01.08.2008
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ISSN1350-7265
DOI10.3150/07-BEJ116

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Abstract Under left truncation, data (X(i), Y(i)) are observed only when Y(i) ≤ X(i). Usually, the distribution function F of the X(i) is the target of interest. In this paper, we study linear functionals ∫ φ dF(n) of the nonparametric maximum likelihood estimator (MLE) of F, the Lynden-Bell estimator F(n). A useful representation of ∫ φ dF(n) is derived which yields asymptotic normality under optimal moment conditions on the score function φ. No continuity assumption on F is required. As a by-product, we obtain the distributional convergence of the Lynden-Bell empirical process on the whole real line.
AbstractList Under left truncation, data (X(i), Y(i)) are observed only when Y(i) ≤ X(i). Usually, the distribution function F of the X(i) is the target of interest. In this paper, we study linear functionals ∫ φ dF(n) of the nonparametric maximum likelihood estimator (MLE) of F, the Lynden-Bell estimator F(n). A useful representation of ∫ φ dF(n) is derived which yields asymptotic normality under optimal moment conditions on the score function φ. No continuity assumption on F is required. As a by-product, we obtain the distributional convergence of the Lynden-Bell empirical process on the whole real line.
Author Wang, Jane-Ling
Stute, Winfried
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  givenname: Jane-Ling
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/22844204$$D View this record in MEDLINE/PubMed
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