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...

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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:123456789/10397
Acceso en línea:
https://revistas.uan.edu.co/index.php/ingeuan/article/view/212
https://repositorio.uan.edu.co/handle/123456789/10397
Palabra clave:
Data Fitting
Generalized Lambda Distribution
Minimization Method
Moments
Percentiles
Genetic Algorithms
Rights
License
https://creativecommons.org/licenses/by-nc-sa/4.0
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spelling 2014-03-042024-10-10T02:24:28Z2024-10-10T02:24:28Zhttps://revistas.uan.edu.co/index.php/ingeuan/article/view/212https://repositorio.uan.edu.co/handle/123456789/10397The 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 NARIÑOhttps://revistas.uan.edu.co/index.php/ingeuan/article/view/212/174https://creativecommons.org/licenses/by-nc-sa/4.0http://purl.org/coar/access_right/c_abf2INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 1 Núm. 2 (2011)2346-14462145-0935Data 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/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Moreno Bedoya, David LeonardoFino Puerto, Nelson Ricardo123456789/10397oai:repositorio.uan.edu.co:123456789/103972024-10-14 03:48:48.457metadata.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.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.accessioned.none.fl_str_mv 2024-10-10T02:24:28Z
dc.date.available.none.fl_str_mv 2024-10-10T02:24:28Z
dc.date.none.fl_str_mv 2014-03-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
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 https://revistas.uan.edu.co/index.php/ingeuan/article/view/212
dc.identifier.uri.none.fl_str_mv https://repositorio.uan.edu.co/handle/123456789/10397
url https://revistas.uan.edu.co/index.php/ingeuan/article/view/212
https://repositorio.uan.edu.co/handle/123456789/10397
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://revistas.uan.edu.co/index.php/ingeuan/article/view/212/174
dc.rights.es-ES.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.publisher.es-ES.fl_str_mv UNIVERSIDAD ANTONIO NARIÑO
dc.source.es-ES.fl_str_mv INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 1 Núm. 2 (2011)
dc.source.none.fl_str_mv 2346-1446
2145-0935
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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