Symmetric Multivariate and Related Distributions
Nonfiction, Science & Nature, Mathematics, Statistics
| Author: | Kai Wang Fang | ISBN: | 9781351093941 |
| Publisher: | CRC Press | Publication: | January 18, 2018 |
| Imprint: | Chapman and Hall/CRC | Language: | English |
| Author: | Kai Wang Fang |
| ISBN: | 9781351093941 |
| Publisher: | CRC Press |
| Publication: | January 18, 2018 |
| Imprint: | Chapman and Hall/CRC |
| Language: | English |
Since the publication of the by now classical Johnson and Kotz Continuous Multivariate Distributions (Wiley, 1972) there have been substantial developments in multivariate distribution theory especially in the area of non-normal symmetric multivariate distributions. The book by Fang, Kotz and Ng summarizes these developments in a manner which is accessible to a reader with only limited background (advanced real-analysis calculus, linear algebra and elementary matrix calculus). Many of the results in this field are due to Kai-Tai Fang and his associates and appeared in Chinese publications only.
A thorough literature search was conducted and the book represents the latest work - as of 1988 - in this rapidly developing field of multivariate distributions. The authors are experts in statistical distribution theory.
Since the publication of the by now classical Johnson and Kotz Continuous Multivariate Distributions (Wiley, 1972) there have been substantial developments in multivariate distribution theory especially in the area of non-normal symmetric multivariate distributions. The book by Fang, Kotz and Ng summarizes these developments in a manner which is accessible to a reader with only limited background (advanced real-analysis calculus, linear algebra and elementary matrix calculus). Many of the results in this field are due to Kai-Tai Fang and his associates and appeared in Chinese publications only.
A thorough literature search was conducted and the book represents the latest work - as of 1988 - in this rapidly developing field of multivariate distributions. The authors are experts in statistical distribution theory.
