A missing information principle and \(M\)-estimators in regression analysis with censored and truncated data

DOI10.1214/aos/1176325627zbMath0817.62030OpenAlexW2047234206MaRDI QIDQ1805548

Zhiliang Ying, Tze Leung Lai

Publication date: 2 July 1995

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176325627

zbMATH Keywords

asymptotic normalityright censoringrobustness propertiesleft truncationinfluence functions\(M\)-estimatorsempirical process theoryself-consistencymartingale central limit theoremsdata-dependent score functionsgeneral missing information principle

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35) Linear regression; mixed models (62J05) Central limit and other weak theorems (60F05) Robustness and adaptive procedures (parametric inference) (62F35)

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