Author:
Mathai Arak,Provost Serge,Haubold Hans
Abstract
AbstractIt is assumed that the readers are familiar with the concept of testing statistical hypotheses on the parameters of a real scalar normal distribution or independent real scalar normal distributions. The likelihood ratio criterion is employed for testing various hypotheses on the parameters of one or more real multivariate Gaussian (or normal) distributions. The tests are based on a simple random samples from a multivariate nonsingular Gaussian distribution. The corresponding test criteria for the complex Gaussian case are also provided for given hypotheses.
Publisher
Springer International Publishing
Reference30 articles.
1. T.W. Anderson (2003): An Introduction to Multivariate Statistical Analysis, Third Edition, Wiley, New York.
2. A.W. Davis (1971): Percentile approximations for a class of likelihood ratio criteria, Biometrika, 58, 349–356.
3. A.W. Davis and J.B. Field (1971): Tables of some multivariate test criteria, Technical Report No. 32, Division of Mathematical Statistics, CSIRO, Canberra, Australia.
4. B.P. Korin (1968): On the distribution of a statistic used for testing a covariance matrix, Biomerika, 55, 171–178.
5. A.M. Mathai (1970a): An expansion of Meijer’s G-function in the logarithmic case with applications, Mathematische Nachrichten, 48, 129–139.