Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization
In the context of generalized linear modelling (GLM), the beta regression analysis is used to estimate regression models when the dependent variable lies between (0,1). In this paper, we carried out a model selection process using several information criteria with heuristic optimization. We employed...
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| Format: | Article |
| Language: | English |
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Naim Çağman
2020-12-01
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| Series: | Journal of New Theory |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/1435401 |
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| _version_ | 1850079858664669184 |
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| author | Mehmet Ali Cengiz Emre Dünder |
| author_facet | Mehmet Ali Cengiz Emre Dünder |
| author_sort | Mehmet Ali Cengiz |
| collection | DOAJ |
| description | In the context of generalized linear modelling (GLM), the beta regression analysis is used to estimate regression models when the dependent variable lies between (0,1). In this paper, we carried out a model selection process using several information criteria with heuristic optimization. We employed the differential evolution algorithm as a heuristic optimization method to select the best model for beta regression analysis. The results show that the alternative-type information criteria provide competitive results during the model selection process in beta regression analysis. |
| format | Article |
| id | doaj-art-ebac267b40e249fabe5fb9330466023f |
| institution | DOAJ |
| issn | 2149-1402 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Naim Çağman |
| record_format | Article |
| series | Journal of New Theory |
| spelling | doaj-art-ebac267b40e249fabe5fb9330466023f2025-08-20T02:45:06ZengNaim ÇağmanJournal of New Theory2149-14022020-12-013376842425Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic OptimizationMehmet Ali Cengiz0https://orcid.org/0000-0002-1271-2588Emre Dünder1https://orcid.org/0000-0003-0230-8968ONDOKUZ MAYIS UNIVERSITYONDOKUZ MAYIS ÜNİVERSİTESİIn the context of generalized linear modelling (GLM), the beta regression analysis is used to estimate regression models when the dependent variable lies between (0,1). In this paper, we carried out a model selection process using several information criteria with heuristic optimization. We employed the differential evolution algorithm as a heuristic optimization method to select the best model for beta regression analysis. The results show that the alternative-type information criteria provide competitive results during the model selection process in beta regression analysis.https://dergipark.org.tr/en/download/article-file/1435401beta regressiondifferential evolution algorithminformation criteriamodel selection |
| spellingShingle | Mehmet Ali Cengiz Emre Dünder Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization Journal of New Theory beta regression differential evolution algorithm information criteria model selection |
| title | Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization |
| title_full | Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization |
| title_fullStr | Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization |
| title_full_unstemmed | Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization |
| title_short | Model Selection in Beta Regression Analysis Using Several Information Criteria and Heuristic Optimization |
| title_sort | model selection in beta regression analysis using several information criteria and heuristic optimization |
| topic | beta regression differential evolution algorithm information criteria model selection |
| url | https://dergipark.org.tr/en/download/article-file/1435401 |
| work_keys_str_mv | AT mehmetalicengiz modelselectioninbetaregressionanalysisusingseveralinformationcriteriaandheuristicoptimization AT emredunder modelselectioninbetaregressionanalysisusingseveralinformationcriteriaandheuristicoptimization |