Characterizations of Recently Introduced Univariate Continuous Distributions II
Author | : G. G. Hamedani |
Publisher | : |
Total Pages | : 436 |
Release | : 2019-05-17 |
ISBN-10 | : 1536150959 |
ISBN-13 | : 9781536150957 |
Rating | : 4/5 (59 Downloads) |
Download or read book Characterizations of Recently Introduced Univariate Continuous Distributions II written by G. G. Hamedani and published by . This book was released on 2019-05-17 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is, as far as the author has gathered, the second of its kind (the first one was published by Nova in 2017 with coauthors Hamedani and Maadooliat) which presents various characterizations of a wide variety of continuous distributions. These two monographs could also be used as sources to prevent reinventing and duplicating the already exiting distributions. This current book consists of seven chapters. The first chapter lists cumulative and density functions of two hundred univariate distributions. Chapter two provides characterizations of these distributions: (i) based on the ration of two truncated moments; (ii) in terms of the hazard function; (iii) in terms of the reverse hazard function; (iv) based on the conditional expectation of certain functions of the random variable. Chapter three includes the characterizations of twenty distributions, including a published paper (Hamedani and Safavimanesh, 2017). Chapter four presents characterizations of thirty six distributions, and contains a published paper (Hamedani, 2017). Chapter five covers the characterizations of forty one distributions, as well as a published paper (Hamedani, 2018a). Chapter six presents characterizations of eighty distributions, and also contains a published paper (Hamedani, 2018b). Finally, chapter seven consists of seventy proposed distributions. The main reason to include previously published papers in Chapters 3-6 is to provide a rather complete source for the interested researchers who would want to avoid reinventing the existing distributions.