A Gamma-Type Distribution with Applications
| datacite.alternateIdentifier.citation | SYMMETRY-BASEL,Vol.12,,2020 | |
| datacite.alternateIdentifier.doi | 10.3390/sym12050870 | |
| datacite.creator | Iriarte, Yuri A. | |
| datacite.creator | Varela, Hector | |
| datacite.creator | Gomez, Hector J. | |
| datacite.creator | Gomez, Hector W. | |
| datacite.date | 2020 | |
| datacite.subject.english | asymmetry | |
| datacite.subject.english | generalized gamma distribution | |
| datacite.subject.english | kurtosis | |
| datacite.subject.english | maximum likelihood estimation | |
| datacite.subject.english | slash distribution | |
| datacite.title | A Gamma-Type Distribution with Applications | |
| dc.date.accessioned | 2021-04-30T16:34:19Z | |
| dc.date.available | 2021-04-30T16:34:19Z | |
| dc.description.abstract | This article introduces a new probability distribution capable of modeling positive data that present different levels of asymmetry and high levels of kurtosis. A slashed quasi-gamma random variable is defined as the quotient of independent random variables, a generalized gamma is the numerator, and a power of a standard uniform variable is the denominator. The result is a new three-parameter distribution (scale, shape, and kurtosis) that does not present the identifiability problem presented by the generalized gamma distribution. Maximum likelihood (ML) estimation is implemented for parameter estimation. The results of two real data applications revealed a good performance in real settings. | |
| dc.identifier.uri | http://repositoriodigital.uct.cl/handle/10925/3029 | |
| dc.language.iso | en | |
| dc.publisher | MDPI | |
| dc.source | SYMMETRY-BASEL | |
| oaire.resourceType | Article | |
| uct.catalogador | WOS | |
| uct.indizacion | SCI |