Minimax estimation in linear regression with singular covariance structure and convex polyhedral constraints

DOI10.1016/0378-3758(93)90123-NzbMath0778.62061OpenAlexW1975376739MaRDI QIDQ689386

Götz Trenkler, Peter Stahlecker

Publication date: 2 December 1993

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(93)90123-n

zbMATH Keywords

singular covariance matrixapproximate linear minimax estimationconvex, compact polyhedral constraintsexact minimax solutiongeneral quadratic losslinear affine minimax estimationmaximal weighted risk

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Minimax procedures in statistical decision theory (62C20)

Related Items

Linear plus quadratic (LPQ) quasiminimax estimation in the linear regression model ⋮ A comparison of minimax and least squares estimators in linear regression with polyhedral prior information ⋮ Minimax adjustment technique in a parameter restricted linear model ⋮ Minimax prediction in the linear model with a relative squared error ⋮ Reducing the Maximum Risk of Regression Estimators by Polyhedral Projection ⋮ Quasi-minimax estimation in the general linear regression model ⋮ Another view of the Kuks-Olman estimator ⋮ Linear regression analysis using the relative squared error ⋮ A minimax approach to missing values in linear regression ⋮ Relative squared error prediction in the generalized linear regression model

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