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Criteria for the equality between ordinary least squares and best linear unbiased estimators under certain linear models
Author(s) -
Baksalary Jerzy K.
Publication year - 1988
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315067
Subject(s) - best linear unbiased prediction , ordinary least squares , mathematics , estimator , least squares function approximation , generalized least squares , linear model , linear least squares , minimum variance unbiased estimator , statistics , computer science , artificial intelligence , selection (genetic algorithm)
A new necessary and sufficient condition is derived for the equality between the ordinary least‐squares estimator and the best linear unbiased estimator of the expectation vector in linear models with certain specific design matrices. This condition is then applied to special cases involving one‐way and two‐way classification models.