Estimating utility functions using generalized maximum entropy
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/8946 https://doi.org/10.1080/02664763.2012.740625 |
Resumo: | This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Estimating utility functions using generalized maximum entropygeneralized maximum entropyutility elicitationMorgenstern utilitymaximum entropy principleThis paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods.Taylor and Francic2013-10-30T11:43:42Z2013-10-302013-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/8946http://hdl.handle.net/10174/8946https://doi.org/10.1080/02664763.2012.740625engPires, C., A. Dionísio, L. Coelho (2013), "Estimating utility functions using GME", Journal of Applied Statistics, 40(1), 221-234.Departamento de Gestãocpires@uevora.ptandreia@uevora.ptlcoelho@uevora.pt637Pires, CesaltinaDionísio, AndreiaCoelho, Luísinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T18:50:30Zoai:dspace.uevora.pt:10174/8946Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:03:07.910018Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Estimating utility functions using generalized maximum entropy |
title |
Estimating utility functions using generalized maximum entropy |
spellingShingle |
Estimating utility functions using generalized maximum entropy Pires, Cesaltina generalized maximum entropy utility elicitation Morgenstern utility maximum entropy principle |
title_short |
Estimating utility functions using generalized maximum entropy |
title_full |
Estimating utility functions using generalized maximum entropy |
title_fullStr |
Estimating utility functions using generalized maximum entropy |
title_full_unstemmed |
Estimating utility functions using generalized maximum entropy |
title_sort |
Estimating utility functions using generalized maximum entropy |
author |
Pires, Cesaltina |
author_facet |
Pires, Cesaltina Dionísio, Andreia Coelho, Luís |
author_role |
author |
author2 |
Dionísio, Andreia Coelho, Luís |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Pires, Cesaltina Dionísio, Andreia Coelho, Luís |
dc.subject.por.fl_str_mv |
generalized maximum entropy utility elicitation Morgenstern utility maximum entropy principle |
topic |
generalized maximum entropy utility elicitation Morgenstern utility maximum entropy principle |
description |
This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-10-30T11:43:42Z 2013-10-30 2013-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/8946 http://hdl.handle.net/10174/8946 https://doi.org/10.1080/02664763.2012.740625 |
url |
http://hdl.handle.net/10174/8946 https://doi.org/10.1080/02664763.2012.740625 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pires, C., A. Dionísio, L. Coelho (2013), "Estimating utility functions using GME", Journal of Applied Statistics, 40(1), 221-234. Departamento de Gestão cpires@uevora.pt andreia@uevora.pt lcoelho@uevora.pt 637 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Taylor and Francic |
publisher.none.fl_str_mv |
Taylor and Francic |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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_version_ |
1799136514939027456 |