Linear Prediction Sufficiency for New Observations in the General Gauss–Markov Model
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Publication:5484682
DOI10.1080/03610920600672146zbMath1102.62072OpenAlexW2034156058MaRDI QIDQ5484682
Publication date: 21 August 2006
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610920600672146
BLUPBLUElinear predictionGauss-Markov modellinear sufficiencylinear zero functionsfundamental equations of best linear unbiased predictionlinear prediction sufficiency
Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Sufficient statistics and fields (62B05)
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Cites Work
- Linear transformations preserving best linear unbiased estimators in a general Gauss-Markoff model
- A note on the concepts of linear and quadratic sufficiency
- The matrix handling of BLUE and BLUP in the mixed linear model
- Representations of best linear unbiased estimators in the Gauss-Markoff model with a singular dispersion matrix
- That BLUP is a good thing: The estimation of random effects. With comments and a rejoinder by the author
- Linear sufficiency with respect to a given vector of parametric functions
- Sufficiency and completeness in the linear model
- The general Gauss-Markov model with possibly singular dispersion matrix
- On best unbiased prediction and its relationships to unbiased estimation
- Best Linear Unbiased Prediction in the Generalized Linear Regression Model
- The Gauss–Markov Theorem for Regression Models with Possibly Singular Covariances
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