Testing for the Equality of the Variance‐Covariance Matrices of Two Jointly Normal Vector Variables | Zendy

Smith Patricia L. | Zendy; Kshiesagar Anant M. | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Testing for the Equality of the Variance‐Covariance Matrices of Two Jointly Normal Vector Variables

Author(s) -

Smith Patricia L.,

Kshiesagar Anant M.

Publication year - 1985

Publication title -

biometrical journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.108

H-Index - 63

eISSN - 1521-4036

pISSN - 0323-3847

DOI - 10.1002/bimj.4710270514

Subject(s) - mathematics , covariance , multivariate normal distribution , variance (accounting) , covariance matrix , statistics , restricted maximum likelihood , likelihood ratio test , combinatorics , random variate , maximum likelihood , random variable , multivariate statistics , accounting , business

Assume a joint 2P ‐variate normal distribution for the p ‐component vectors x and y with unknown mean and unknown variance‐covariance matrices Σ xx and Σ yy respectively, with Cov ( x , y )=Σ xy . No assumptions are made about the nature of Σ xy . The likelihood ratio method is investigated to test the hypothesis that Σ xx = Σ yy . A method of numerical solution to the likelihood equations in the restricted parameter case is given when p =2, and approximate solutions are suggested for p >2.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research