A new family of generalized distributions

Kumaraswamy [Generalized probability density-function for double-bounded random-processes, J. Hydrol. 462 (1980), pp. 79-88] introduced a distribution for double-bounded random processes with hydrological applications. For the first time, based on this distribution, we describe a new family of gener...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of statistical computation and simulation Ročník 81; číslo 7; s. 883 - 898
Hlavní autoři: Cordeiro, Gauss M., de Castro, Mário
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 01.07.2011
Taylor & Francis Ltd
Témata:
Computer simulation
gamma distribution
Inverse
Kumaraswamy distribution
Mathematical analysis
Mathematical models
Maximum likelihood method
moments
Normal distribution
order statistics
Parents
Probability distribution
Random processes
Statistics
Weibull distribution
ISSN:0094-9655, 1563-5163
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Kumaraswamy [Generalized probability density-function for double-bounded random-processes, J. Hydrol. 462 (1980), pp. 79-88] introduced a distribution for double-bounded random processes with hydrological applications. For the first time, based on this distribution, we describe a new family of generalized distributions (denoted with the prefix 'Kw') to extend the normal, Weibull, gamma, Gumbel, inverse Gaussian distributions, among several well-known distributions. Some special distributions in the new family such as the Kw-normal, Kw-Weibull, Kw-gamma, Kw-Gumbel and Kw-inverse Gaussian distribution are discussed. We express the ordinary moments of any Kw generalized distribution as linear functions of probability weighted moments (PWMs) of the parent distribution. We also obtain the ordinary moments of order statistics as functions of PWMs of the baseline distribution. We use the method of maximum likelihood to fit the distributions in the new class and illustrate the potentiality of the new model with an application to real data.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:0094-9655
1563-5163
DOI:10.1080/00949650903530745