Properties of the bivariate confluent hypergeometric function kind 1 distribution
ABSTRACT: The bivariate confluent hypergeometric function kind 1 distribution is defined by the probability density function proportional to x1ν1 − 1 x2ν2 − 11F1(α; β; −x1 − x2). In this article, we study several properties of this distribution and derive density functions of X1/X2, X1/(X1 + X2), X1...
- Autores:
-
Nagar, Daya Krishna
Sepulveda Murillo, Fabio Humberto
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2011
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/30378
- Acceso en línea:
- https://hdl.handle.net/10495/30378
- Palabra clave:
- Distribución hipergeométrica
Hypergeometric distribution
Funciones hipergeométricas
Functions, hypergeometric
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by/2.5/co/
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Properties of the bivariate confluent hypergeometric function kind 1 distribution |
| title |
Properties of the bivariate confluent hypergeometric function kind 1 distribution |
| spellingShingle |
Properties of the bivariate confluent hypergeometric function kind 1 distribution Distribución hipergeométrica Hypergeometric distribution Funciones hipergeométricas Functions, hypergeometric |
| title_short |
Properties of the bivariate confluent hypergeometric function kind 1 distribution |
| title_full |
Properties of the bivariate confluent hypergeometric function kind 1 distribution |
| title_fullStr |
Properties of the bivariate confluent hypergeometric function kind 1 distribution |
| title_full_unstemmed |
Properties of the bivariate confluent hypergeometric function kind 1 distribution |
| title_sort |
Properties of the bivariate confluent hypergeometric function kind 1 distribution |
| dc.creator.fl_str_mv |
Nagar, Daya Krishna Sepulveda Murillo, Fabio Humberto |
| dc.contributor.author.none.fl_str_mv |
Nagar, Daya Krishna Sepulveda Murillo, Fabio Humberto |
| dc.contributor.researchgroup.spa.fl_str_mv |
Análisis Multivariado |
| dc.subject.lemb.none.fl_str_mv |
Distribución hipergeométrica Hypergeometric distribution Funciones hipergeométricas Functions, hypergeometric |
| topic |
Distribución hipergeométrica Hypergeometric distribution Funciones hipergeométricas Functions, hypergeometric |
| description |
ABSTRACT: The bivariate confluent hypergeometric function kind 1 distribution is defined by the probability density function proportional to x1ν1 − 1 x2ν2 − 11F1(α; β; −x1 − x2). In this article, we study several properties of this distribution and derive density functions of X1/X2, X1/(X1 + X2), X1 + X2 and 2 √(X1 X2). The density function of 2 √(X1 X2) is represented in terms of modified Bessel function of the second kind. We also show that for ν1 − ν2 = 1/2, 2 √(X1 X2) follows a confluent hypergeometric function kind 1 distribution. |
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2011 |
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2011 |
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2022-09-02T17:24:10Z |
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2022-09-02T17:24:10Z |
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Artículo de investigación |
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1669-9637 |
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eng |
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eng |
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Rev. Unión Mat. Argent. |
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Revista de la Unión Matemática Argentina |
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Nagar, Daya KrishnaSepulveda Murillo, Fabio HumbertoAnálisis Multivariado2022-09-02T17:24:10Z2022-09-02T17:24:10Z20110041-6932https://hdl.handle.net/10495/303781669-9637ABSTRACT: The bivariate confluent hypergeometric function kind 1 distribution is defined by the probability density function proportional to x1ν1 − 1 x2ν2 − 11F1(α; β; −x1 − x2). In this article, we study several properties of this distribution and derive density functions of X1/X2, X1/(X1 + X2), X1 + X2 and 2 √(X1 X2). The density function of 2 √(X1 X2) is represented in terms of modified Bessel function of the second kind. We also show that for ν1 − ν2 = 1/2, 2 √(X1 X2) follows a confluent hypergeometric function kind 1 distribution.COL000053211application/pdfengUnión Matemática ArgentinaBahía Blanca, Argentinahttp://creativecommons.org/licenses/by/2.5/co/https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Properties of the bivariate confluent hypergeometric function kind 1 distributionArtículo de investigaciónhttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionDistribución hipergeométricaHypergeometric distributionFunciones hipergeométricasFunctions, hypergeometricRev. Unión Mat. Argent.2111152Revista de la Unión Matemática ArgentinaPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://bibliotecadigital.udea.edu.co/bitstreams/1a67f7d9-6c44-45d8-8b2b-babcff825015/download8a4605be74aa9ea9d79846c1fba20a33MD53falseAnonymousREADORIGINALNagarDaya_2011_PropertiesBivariateConfluent .pdfNagarDaya_2011_PropertiesBivariateConfluent .pdfArtículo de investigaciónapplication/pdf164139https://bibliotecadigital.udea.edu.co/bitstreams/832dc4d2-719f-4b5c-baab-e252f8544ca2/downloadc5639d0c9fd88550a946708134f78dbbMD51trueAnonymousREADCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8927https://bibliotecadigital.udea.edu.co/bitstreams/5062853e-0737-4a3d-bffd-d1ffe5d62d41/download1646d1f6b96dbbbc38035efc9239ac9cMD52falseAnonymousREADTEXTNagarDaya_2011_PropertiesBivariateConfluent .pdf.txtNagarDaya_2011_PropertiesBivariateConfluent .pdf.txtExtracted texttext/plain23963https://bibliotecadigital.udea.edu.co/bitstreams/1caa4551-9795-4e86-bf3b-c48cb5a8f3d4/download2c51f4e0c724822b492718163030f4e4MD54falseAnonymousREADTHUMBNAILNagarDaya_2011_PropertiesBivariateConfluent .pdf.jpgNagarDaya_2011_PropertiesBivariateConfluent .pdf.jpgGenerated Thumbnailimage/jpeg9424https://bibliotecadigital.udea.edu.co/bitstreams/f87f9f0c-e9c4-44bd-8ef2-4262075886b8/downloadb5f002c6cada7e39cd07ffc74fbeef30MD55falseAnonymousREAD10495/30378oai:bibliotecadigital.udea.edu.co:10495/303782025-03-26 22:26:18.329http://creativecommons.org/licenses/by/2.5/co/open.accesshttps://bibliotecadigital.udea.edu.coRepositorio Institucional de la Universidad de Antioquiaaplicacionbibliotecadigitalbiblioteca@udea.edu.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 |