Simple least squares estimation versus best linear unbiased prediction

DOI10.1016/0378-3758(81)90024-0zbMath0476.62057OpenAlexW1994003537MaRDI QIDQ1159932

Jerzy K. Baksalary, Radosław Kala

Publication date: 1981

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(81)90024-0

zbMATH Keywords

linear modelbest linear unbiased estimator

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Related Items (5)

A note on the inverse-partitioned-matrix method in linear regression analysis ⋮ Matrix rank and inertia formulas in the analysis of general linear models ⋮ Memory-based reduced modelling and data-based estimation of opinion spreading ⋮ On equality of ordinary least squares estimator, best linear unbiased estimator and best linear unbiased predictor in the general linear model ⋮ Equalities between OLSE, BLUE and BLUP in the linear model

Cites Work

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• Comparison of Least Squares and Minimum Variance Estimates of Regression Parameters
• Linear Spaces and Minimum Variance Unbiased Estimation
• Prediction and the efficiency of least squares
• An Extension of the Generalized Inverse of a Matrix
• The Gauss–Markov Theorem for Regression Models with Possibly Singular Covariances
• Conditions for Positive and Nonnegative Definiteness in Terms of Pseudoinverses

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