scholarly journals Testing hypotheses about covariance matrices in general MANOVA designs

2021 ◽  
Author(s):  
Paavo Sattler ◽  
Arne Bathke ◽  
Markus Pauly
Keyword(s):  
Covariance Matrices ◽  
Testing Hypotheses

scholarly journals Testing Hypotheses Of Covariance Structure In Multivariate Data

2018 ◽  
Vol 33 ◽  
pp. 53-62 ◽  
Author(s):  
Miguel Fonseca ◽  
Arkadiusz Koziol ◽  
Roman Zmyslony
Keyword(s):  
Multivariate Data ◽  
Covariance Structure ◽  
Covariance Matrices ◽  
Multivariate Models ◽  
F Test ◽  
New Approach ◽  
Testing Hypotheses ◽  
Unbiased Estimators

In this paper there is given a new approach for testing hypotheses on the structure of covariance matrices in double multivariate data. It is proved that ratio of positive and negative parts of best unbiased estimators (BUE) provide an F-test for independence of blocks variables in double multivariate models.

Testing hypotheses about ordinal interactions: Simulations and further comments.

1989 ◽  
Vol 74 (2) ◽  
pp. 247-252 ◽  
Author(s):  
Michael J. Strube ◽  
Philip Bobko
Keyword(s):  
Testing Hypotheses

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

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