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Continuous Multivariate Distributions: Models and Applications (Volume 1)

By: Kotz, Samuel (ed.) | Balakrishnan, N. (ed.) | Johnson, Norman L.J. (ed.).
Material type: materialTypeLabelBookSeries: Continuous Multivariate Distributions (Volume 1). Wiley Series in Probability and Statistics.Publisher: Chichester [u.a.] , Wiley, 2000Edition: 2nd edition.ISBN: 0-471-18387-3.Subject(s): distribution | multivariate analysis | probability theorySummary: Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth coverage includes MV systems of distributions, MV normal, MV exponential, MV extreme value, MV beta, MV gamma, MV logistic, MV Liouville, and MV Pareto distributions, as well as MV natural exponential families, which have grown immensely since the 1970s. Each distribution is presented in its own chapter along with descriptions of real-world applications gleaned from the current literature on continuous multivariate distributions and their applications
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Anthology Library
C1 73 (Browse shelf) Available 79181

Continuous Multivariate Distributions, Volume 1, Second Edition provides a remarkably comprehensive, self-contained resource for this critical statistical area. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, inferential procedures, computational and simulational aspects, and applications of continuous multivariate distributions. In-depth coverage includes MV systems of distributions, MV normal, MV exponential, MV extreme value, MV beta, MV gamma, MV logistic, MV Liouville, and MV Pareto distributions, as well as MV natural exponential families, which have grown immensely since the 1970s. Each distribution is presented in its own chapter along with descriptions of real-world applications gleaned from the current literature on continuous multivariate distributions and their applications

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