Titlebook: Elliptically Contoured Models in Statistics and Portfolio Theory; Arjun K. Gupta,Tamas Varga,Taras Bodnar Book 2013Latest edition Springer

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Hypothesis TestingBefore studying concrete hypotheses, we derive some general theorems. These results are based on Anderson, Fang, and Hsu (.) and Hsu (.).

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https://doi.org/10.1007/978-3-662-28439-1mal distributionsis defined in this chapter. Furthermore, we present another way to obtain the p.d.f. of a matrix variate elliptically contoured distribution from the density functions of matrix variate normal distributions. For this purpose, Laplace transform is used.

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Mixtures of Normal Distributionsmal distributionsis defined in this chapter. Furthermore, we present another way to obtain the p.d.f. of a matrix variate elliptically contoured distribution from the density functions of matrix variate normal distributions. For this purpose, Laplace transform is used.

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Preliminariesese distributions provedto be useful in statistical inference. For example, the Wishart distribution is essential when studying the sample covariance matrix in the multivariate normal theory. Random matricescan also be used to describe repeated measurements on multivariate variables. In this case,th

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Basic Propertiesand Sutradhar and Ali(1989). We use the definition given in Gupta and Varga (1994b). Moreover, we present somebasic properties of matrix variate elliptically contoured distributions, such as the stochasticrepresentation, the conditional and marginal distributions. Finally, several families of matrix