Representations of efficient score for coarse data problems based on Neumann series expansion

DOI10.1007/s10463-009-0231-7zbMath1333.62268OpenAlexW2102634896WikidataQ33981187 ScholiaQ33981187MaRDI QIDQ261830

Hua Yun Chen

Publication date: 24 March 2016

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3148113

zbMATH Keywords

projectionauxiliary variableadjoint operatorcoarsening at randomnonparametric information operator

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Point estimation (62F10) Censored data models (62N01)

Cites Work

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• Ignorability and coarse data
• Unified methods for censored longitudinal data and causality
• Information bounds for Cox regression models with missing data.
• Estimation and Inference Based on Neumann Series Approximation to Locally Efficient Score in Missing Data Problems
• Inference and missing data
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Ignorability in general incomplete-data models
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models

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