The structure of a linear model: sufficiency, ancillarity, invariance, equivariance, and the normal distribution

DOI10.1006/JMVA.1999.1871zbMath1065.62534OpenAlexW2044167861MaRDI QIDQ1578057

Wolfgang Bischoff

Publication date: 2000

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1999.1871

zbMATH Keywords

invariancelinear modelequivariancesufficiencyancillarityspecific sufficiencynormal distribution, partially known covariance matrices

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Foundations and philosophical topics in statistics (62A01) Sufficient statistics and fields (62B05)

Related Items (2)

A characterization of a Gaussian process in terms of sufficient estimators ⋮ A decomposition of a linear model

Cites Work

• Invariantly sufficient equivariant statistics and characterizations of normality in translation classes
• A characterization of the normal distribution by sufficiency of the least squares estimation
• Nuisance parameters in statistics of finance
• Lower Bounds for the Efficiency of Designs with Respect to theD-Criterion when the Observations are Correlated
• A Sufficient Statistics Characterization of the Normal Distribution
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