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dc.contributor.authorAndrade, Bernardo Borba dept_BR
dc.contributor.authorSouza, Geraldo da Silva ept_BR
dc.date.accessioned2020-01-24T10:30:12Z-
dc.date.available2020-01-24T10:30:12Z-
dc.date.issued2019pt_BR
dc.identifier.citationANDRADE, Bernardo B. de; SOUZA, Geraldo S. The EM algorithm for standard stochastic frontier models. Pesquisa Operacional, v. 39, n. 3, p. 361-378, 2019. DOI: https://doi.org/10.1590/0101-7438.2019.039.03.0361. Disponível em: http://scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382019000300361. Acesso em: 23 jan. 2020.pt_BR
dc.identifier.urihttps://repositorio.unb.br/handle/10482/36305-
dc.language.isoenpt_BR
dc.publisherSociedade Brasileira de Pesquisa Operacionalpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleThe EM algorithm for standard stochastic frontier modelspt_BR
dc.typeArtigopt_BR
dc.subject.keywordEficiênciapt_BR
dc.subject.keywordAceleração EMpt_BR
dc.subject.keywordProbabilidadespt_BR
dc.subject.keywordAlgoritmos-
dc.rights.license(CC BY) - © 2019 Brazilian Operations Research Society-
dc.identifier.doihttps://doi.org/10.1590/0101-7438.2019.039.03.0361pt_BR
dc.description.abstract1The Expectation-Maximization (EM) algorithm is developed for the stochastic frontier models most used in practice with cross-section data. The resulting algorithms can be easily programmed into a computer and are shown to be worthy alternatives to general-purpose optimization routines currently used. The algorithms for the half normal and the exponential models have closed-form expressions whereas those for the truncated normal and gamma models will require the numerical solution of a nonlinear equation. Implementations of the EM algorithm either as a stand-alone routine or in accelerated form and also combined with Newton-like methods are discussed. We provide illustrations, along with R tools, for cost and production frontiers.-
dc.identifier.orcidhttp://orcid.org/0000-0003-4688-9733-
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