Preliminary test estimation of a vector of parametric functions in the general Gauss--Markov model

DOI10.1016/0378-3758(93)90126-QzbMath0779.62047OpenAlexW2022461197MaRDI QIDQ689391

Jerzy K. Baksalary, Pawel R. Pordzik

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)90126-q

zbMATH Keywords

nuisance parametersblock designpreliminary test estimationgeneral Gauss-Markov modelfixed- effects modelgiven vector of estimable parametric functionsmain parametersmatrix riskssingular linear modelvector of treatment contrasts

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15)

Related Items (2)

Pre-test estimation in the linear regression model with competing restrictions ⋮ On pre-test estimation of parametric functions in the general Gauss-Markov model with quadratic risk

Cites Work

• Inverse-partitioned-matrix method for the general Gauss-Markov model with linear restrictions
• Estimability in partitioned linear models
• Implied linear restrictions in the general Gauss-Markov model
• Mean square error tests for restrictions in singular linear models
• A study of the equivalence between a gauss-markoff model and its augmentation by nuisance parameters
• Eine anmerkung zu der arbeit ”ldentifiability and estimabllity“ von H. Bunke und O.Bunke
• The Statistical Consequences of Preliminary Test Estimators in Regression
• A Test of the Mean Square Error Criterion for Restrictions in Linear Regression
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