Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments”
The generalized lambda distribution, λ,λ,λ,λ(GLD ) 1 432 is a four-parameter family that has been used for fitting distributions to a wide variety of data sets. Minimization through traditional calculus-based methods has been implemented with relative success, but due to computational and theoretica...
- Autores:
-
Moreno Bedoya, David Leonardo
Fino Puerto, Nelson Ricardo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2014
- Institución:
- Universidad Antonio Nariño
- Repositorio:
- Repositorio UAN
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uan.edu.co:123456789/3892
- Acceso en línea:
- http://revistas.uan.edu.co/index.php/ingeuan/article/view/212
http://repositorio.uan.edu.co/handle/123456789/3892
- Palabra clave:
- Data Fitting
Generalized Lambda Distribution
Minimization Method
Moments
Percentiles
Genetic Algorithms
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Moreno Bedoya, David LeonardoFino Puerto, Nelson Ricardo2021-06-16T13:52:56Z2021-06-16T13:52:56Z2014-03-04http://revistas.uan.edu.co/index.php/ingeuan/article/view/212http://repositorio.uan.edu.co/handle/123456789/3892The generalized lambda distribution, λ,λ,λ,λ(GLD ) 1 432 is a four-parameter family that has been used for fitting distributions to a wide variety of data sets. Minimization through traditional calculus-based methods has been implemented with relative success, but due to computational and theoretical shortcomings of those methods, the moment space has been limited. This paper solve those troubles by using Genetic Algorithms (search algorithms based on the mechanics of natural selection and natural genetics) applied to the methods of moments. Examples of better solutions than the ones find out with traditional calculusbased methods are included.The generalized lambda distribution, λ,λ,λ,λ(GLD ) 1 432 is a four-parameter family that has been used for fitting distributions to a wide variety of data sets. Minimization through traditional calculus-based methods has been implemented with relative success, but due to computational and theoretical shortcomings of those methods, the moment space has been limited. This paper solve those troubles by using Genetic Algorithms (search algorithms based on the mechanics of natural selection and natural genetics) applied to the methods of moments. Examples of better solutions than the ones find out with traditional calculusbased methods are included.application/pdfspaUniversidad Antonio Nariñohttp://revistas.uan.edu.co/index.php/ingeuan/article/view/212/1742346-14462145-0935INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 1 Núm. 2 (2011)Data FittingGeneralized Lambda DistributionMinimization MethodMomentsPercentilesGenetic AlgorithmsUsing genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments”Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments”info:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85123456789/3892oai:repositorio.uan.edu.co:123456789/38922024-10-09 22:54:59.539https://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abiertometadata.onlyhttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co |
| dc.title.en-US.fl_str_mv |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| dc.title.es-ES.fl_str_mv |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| title |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| spellingShingle |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” Data Fitting Generalized Lambda Distribution Minimization Method Moments Percentiles Genetic Algorithms |
| title_short |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| title_full |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| title_fullStr |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| title_full_unstemmed |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| title_sort |
Using genetic algorithms as a parameter estimation tool for the generalized lambda distribution (gld) family: “methods of moments” |
| dc.creator.fl_str_mv |
Moreno Bedoya, David Leonardo Fino Puerto, Nelson Ricardo |
| dc.contributor.author.spa.fl_str_mv |
Moreno Bedoya, David Leonardo Fino Puerto, Nelson Ricardo |
| dc.subject.en-US.fl_str_mv |
Data Fitting Generalized Lambda Distribution Minimization Method Moments Percentiles Genetic Algorithms |
| topic |
Data Fitting Generalized Lambda Distribution Minimization Method Moments Percentiles Genetic Algorithms |
| description |
The generalized lambda distribution, λ,λ,λ,λ(GLD ) 1 432 is a four-parameter family that has been used for fitting distributions to a wide variety of data sets. Minimization through traditional calculus-based methods has been implemented with relative success, but due to computational and theoretical shortcomings of those methods, the moment space has been limited. This paper solve those troubles by using Genetic Algorithms (search algorithms based on the mechanics of natural selection and natural genetics) applied to the methods of moments. Examples of better solutions than the ones find out with traditional calculusbased methods are included. |
| publishDate |
2014 |
| dc.date.issued.spa.fl_str_mv |
2014-03-04 |
| dc.date.accessioned.none.fl_str_mv |
2021-06-16T13:52:56Z |
| dc.date.available.none.fl_str_mv |
2021-06-16T13:52:56Z |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/212 |
| dc.identifier.uri.none.fl_str_mv |
http://repositorio.uan.edu.co/handle/123456789/3892 |
| url |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/212 http://repositorio.uan.edu.co/handle/123456789/3892 |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.none.fl_str_mv |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/212/174 |
| dc.rights.none.fl_str_mv |
Acceso abierto |
| dc.rights.license.spa.fl_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
| dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc-sa/4.0/ |
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info:eu-repo/semantics/openAccess |
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http://purl.org/coar/access_right/c_abf2 |
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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Acceso abierto https://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Antonio Nariño |
| dc.source.none.fl_str_mv |
2346-1446 2145-0935 |
| dc.source.es-ES.fl_str_mv |
INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 1 Núm. 2 (2011) |
| institution |
Universidad Antonio Nariño |
| repository.name.fl_str_mv |
Repositorio Institucional UAN |
| repository.mail.fl_str_mv |
alertas.repositorio@uan.edu.co |
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1814300358695976960 |