A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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/10400.5/28349 |
Resumo: | Partial frontiers have been recently developed in order to overcome several drawbacks of the traditional nonparametric techniques. These robust frontier (order-a and order-m) methods avoid the curse of dimensionality, are less sensitive to outliers and extreme data and may include direct environmental information in the model. Nonetheless, the disadvantages of these partial frontier-based methods according to the formulation proposed in the literature are that they do not allow weight restrictions or non-variable returns to scale technology. The procedure here proposed is an extension of the traditional order-a method, allowing the estimation of an empirical convex a-level, assuming also some additional constraints, such as the virtual weight restrictions and non-variable returns to scale. In the particular case of nonconvex attainable sets, unrestricted formulations and variable returns to scale assumption, the proposed procedure returns the same results as the standard order-a |
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A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scaleData envelopment analysis, Order-a, Free disposal hull, Weight restrictions, Returns to scale, ConvexityPartial frontiers have been recently developed in order to overcome several drawbacks of the traditional nonparametric techniques. These robust frontier (order-a and order-m) methods avoid the curse of dimensionality, are less sensitive to outliers and extreme data and may include direct environmental information in the model. Nonetheless, the disadvantages of these partial frontier-based methods according to the formulation proposed in the literature are that they do not allow weight restrictions or non-variable returns to scale technology. The procedure here proposed is an extension of the traditional order-a method, allowing the estimation of an empirical convex a-level, assuming also some additional constraints, such as the virtual weight restrictions and non-variable returns to scale. In the particular case of nonconvex attainable sets, unrestricted formulations and variable returns to scale assumption, the proposed procedure returns the same results as the standard order-aRepositório da Universidade de LisboaFerreira, DiogoMarques, Rui Cunha2023-08-31T13:28:30Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/28349engFerreira, D.C., Marques, R.C. A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale. Oper Res Int J 20, 1011–1046 (2020). https://doi.org/10.1007/s12351-017-0370-110.1007/s12351-017-0370-1info: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:RCAAP2023-09-03T01:32:03Zoai:www.repository.utl.pt:10400.5/28349Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:28:09.790493Repositório 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 |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale |
title |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale |
spellingShingle |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale Ferreira, Diogo Data envelopment analysis, Order-a, Free disposal hull, Weight restrictions, Returns to scale, Convexity |
title_short |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale |
title_full |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale |
title_fullStr |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale |
title_full_unstemmed |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale |
title_sort |
A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale |
author |
Ferreira, Diogo |
author_facet |
Ferreira, Diogo Marques, Rui Cunha |
author_role |
author |
author2 |
Marques, Rui Cunha |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Ferreira, Diogo Marques, Rui Cunha |
dc.subject.por.fl_str_mv |
Data envelopment analysis, Order-a, Free disposal hull, Weight restrictions, Returns to scale, Convexity |
topic |
Data envelopment analysis, Order-a, Free disposal hull, Weight restrictions, Returns to scale, Convexity |
description |
Partial frontiers have been recently developed in order to overcome several drawbacks of the traditional nonparametric techniques. These robust frontier (order-a and order-m) methods avoid the curse of dimensionality, are less sensitive to outliers and extreme data and may include direct environmental information in the model. Nonetheless, the disadvantages of these partial frontier-based methods according to the formulation proposed in the literature are that they do not allow weight restrictions or non-variable returns to scale technology. The procedure here proposed is an extension of the traditional order-a method, allowing the estimation of an empirical convex a-level, assuming also some additional constraints, such as the virtual weight restrictions and non-variable returns to scale. In the particular case of nonconvex attainable sets, unrestricted formulations and variable returns to scale assumption, the proposed procedure returns the same results as the standard order-a |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 2020-01-01T00:00:00Z 2023-08-31T13:28:30Z |
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/10400.5/28349 |
url |
http://hdl.handle.net/10400.5/28349 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ferreira, D.C., Marques, R.C. A step forward on order-α robust nonparametric method: inclusion of weight restrictions, convexity and non-variable returns to scale. Oper Res Int J 20, 1011–1046 (2020). https://doi.org/10.1007/s12351-017-0370-1 10.1007/s12351-017-0370-1 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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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1799133549830340608 |