A new bound for the Euclidean norm of the difference between the least squares and the best linear unbiased estimators

DOI10.1214/aos/1176345018zbMath0464.62055OpenAlexW4244039957MaRDI QIDQ1154194

Jerzy K. Baksalary, Radosław Kala

Publication date: 1980

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1176345018

zbMATH Keywords

linear modelleast squares estimatorbest linear unbiased estimatorspectral matrix normEuclidean vector norm

Mathematics Subject Classification ID

Linear regression; mixed models (62J05)

Related Items (8)

On ordinary least-squares methods for sample surveys ⋮ Some Further Remarks on the Linear Sufficiency in the Linear Model ⋮ Comparing the BLUEs Under Two Linear Models ⋮ Let us do the twist again ⋮ On the performance of the ordinary least squares method under an error component model. ⋮ Upper bounds for the Euclidean distances between the BLUPs ⋮ Equalities between OLSE, BLUE and BLUP in the linear model ⋮ The inefficiency of least squares in Gauss-Markov and variance component models

This page was built for publication: A new bound for the Euclidean norm of the difference between the least squares and the best linear unbiased estimators