A minimax approach to missing values in linear regression

DOI10.1007/BF02925545zbMath0774.62069MaRDI QIDQ3136509

Peter Stahlecker, Michaela Jänner

Publication date: 18 October 1993

Published in: Statistical Papers (Search for Journal in Brave)

zbMATH Keywords

incomplete datalinear modelerrors in variablesminimax estimationprior informationexogenous variablesrisk comparisonsstrategy of deleting missing values

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Minimax procedures in statistical decision theory (62C20)

Related Items (1)

Minimax least squares and quasiminimax estimation in linear models with missing values

Cites Work

• Minimax estimation in linear regression with singular covariance structure and convex polyhedral constraints
• Minimax linear regression estimation with symmetric parameter restrictions
• Full and partial minimax estimation in regression analysis with additional linear constraints
• Characterization of minimax linear estimators in linear regression
• Multiple Regression with Missing Observations among the Independent Variables
• Approximate minimax estimation in linear regression: a simulation study
• Approximate minimax estimation in linear regression: theoretical results
• Quasi minimax estimation in the linear regression model
• Partial Minimax Estimation in Regression Analysis
• Efficiency gains due to using missing data procedures in regression models
• A minimax linear estimator for linear parameters under restrictions in form of inequalities
• Bayes, Admissible, and Minimax Linear Estimators in Linear Models with Restricted Parameter Space
• Estimation of the error-components model with incomplete panels
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