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Symmetrically Dependent Models Arising in Visual Assessment Data
Author(s) -
Viana Marlos,
Olkin Ingram
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.01188.x
Subject(s) - permutation (music) , covariance , mathematics , statistics , inference , maximum likelihood , sample (material) , psychology , artificial intelligence , computer science , physics , acoustics , thermodynamics
Summary. Given data from bilateral visual assessments on N subjects at k occasions, we consider inference for contralateral correlations ( C ) between fellow eyes and lateral correlations ( L ) among p different assessments of the same eye. Under permutation symmetric dependence structure between observations from fellow eyes and among observations from the same eye, we obtain maximum likelihood estimates of L , C , and L –‐ C. Based on the large‐sample estimates of the corresponding covariance structures, we test the hypothesis that the association between fellow eyes is constant across time and the hypothesis that lateral and contralateral associations between any two occasions are the same.