Skip to content

Characterizations of Recently Introduced Univariate Continuous Distributions II

G.G. Hamedani
Barcode 9781536150957
Hardback

Sold out
Original price £184.45 - Original price £184.45
Original price
£184.45
£184.45 - £184.45
Current price £184.45

Click here to join our rewards scheme and earn points on this purchase!

Availability:
Out of stock

Release Date: 17/05/2019

Genre: Science Nature & Math
Label: Nova Science Publishers Inc
Language: English
Publisher: Nova Science Publishers Inc

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. The current book consists of seven chapters. The first chapter lists cumulative and density functions of two hundred and twenty 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, which appeared in a published paper (Hamedani and Safavimanesh, 2017). Chapter four presents characterizations of thirty six distributions, contains a published paper (Hamedani, 2017). Chapter five covers the characterizations of forty one distributions, which appeared in a published paper (Hamedani, 2018a). Chapter six presents characterizations of eighty distributions, contained in 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.