Premium
COMBINING INFORMATION FROM LINEAR MODELS ON POSSIBLY DIFFERENT SCALES
Author(s) -
Berman Mark
Publication year - 1984
Publication title -
australian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 0004-9581
DOI - 10.1111/j.1467-842x.1984.tb01230.x
Subject(s) - covariance matrix , covariance , mathematics , likelihood ratio test , statistical hypothesis testing , statistics , maximum likelihood , statistical model , linear model , generalized linear model , correlation , statistical physics , physics , geometry
Summary This paper examines the joint statistical analysis of M independent data sets, the jth of which satisfies the model λ j Y j =X j B +ε j , where the λ j are unknown and the ε i are normally distributed with a known correlation structure. The maximum likelihood equations, their asymptotic covariance matrix, and the likelihood ratio test of the hypothesis that the λ j s are all equal are derived. These results are applied to two examples.