Edgeworth Expansion and Bootstrap Approximation for Studentized Product-Limit Estimator with Truncated and Censored Data

DOI10.1080/03610920500498840zbMath1093.62019OpenAlexW2120241623MaRDI QIDQ5484655

Bing-Yi Jing, Qi Hua Wang

Publication date: 21 August 2006

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920500498840

zbMATH Keywords

bootstrap approximationproduct-limit estimatorrandom censorshipleft truncation

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05) Censored data models (62N01) Nonparametric statistical resampling methods (62G09) Estimation in survival analysis and censored data (62N02)

Cites Work

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• Some representations of the nonparametric maximum likelihood estimators with truncated data
• Asymptotic properties of the product limit estimate under random truncation
• Strong embedding of the estimator of the distribution function under random censorship
• I.i.d. representations for the bivariate product limit estimators and the bootstrap versions
• Bootstrap methods: another look at the jackknife
• Strong representations of the survival function estimator for truncated and censored data with applications
• On bootstrap accuracy with censored data
• The Edgeworth expansion for U-statistics of degree two
• A large sample study of the life table and product limit estimates under random censorship
• On strong uniform consistency of the Lynden-Bell estimator for truncated data
• The product-limit estimator and the bootstrap: Some asymptotic representations
• Almost sure representations of the product-limit estimator for truncated data
• Edgeworth expansion for the kaplan-meier estimato
• Nonparametric Estimation from Incomplete Observations
• Berry-esseen bound for the kaplan-meier estimator
• A note on the product-limit estimator under right censoring and left truncation
• Estimating a distribution function with truncated data

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