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dc.contributor.authorBastos, Fernando de Souza
dc.contributor.authorBarreto-Souza, Wagner
dc.date.accessioned2020-07-08T06:06:42Z
dc.date.available2020-07-08T06:06:42Z
dc.date.issued2020-06-14
dc.date.submitted2019-10-01
dc.identifier.citationBastos, F. de S., & Barreto-Souza, W. (2020). Birnbaum–Saunders sample selection model. Journal of Applied Statistics, 1–21. doi:10.1080/02664763.2020.1780570
dc.identifier.issn1360-0532
dc.identifier.issn0266-4763
dc.identifier.doi10.1080/02664763.2020.1780570
dc.identifier.urihttp://hdl.handle.net/10754/664079
dc.description.abstractThe sample selection bias problem occurs when the outcome of interest is only observed according to some selection rule, where there is a dependence structure between the outcome and the selection rule. In a pioneering work, J. Heckman proposed a sample selection model based on a bivariate normal distribution for dealing with this problem. Due to the non-robustness of the normal distribution, many alternatives have been introduced in the literature by assuming extensions of the normal distribution like the Student-t and skew-normal models. One common limitation of the existent sample selection models is that they require a transformation of the outcome of interest, which is common (Formula presented.) -valued, such as income and wage. With this, data are analyzed on a non-original scale which complicates the interpretation of the parameters. In this paper, we propose a sample selection model based on the bivariate Birnbaum–Saunders distribution, which has the same number of parameters that the classical Heckman model. Further, our associated outcome equation is (Formula presented.) -valued. We discuss estimation by maximum likelihood and present some Monte Carlo simulation studies. An empirical application to the ambulatory expenditures data from the 2001 Medical Expenditure Panel Survey is presented.
dc.description.sponsorshipWe thank the Associate Editor and two anonymous Referees for their important comments and suggestions which lead to an improvement of this paper. This work is part of the Ph.D. thesis by Fernando de Souza Bastos realized at the Department of Statistics from the Universidade Federal de Minas Gerais.
dc.publisherInforma UK Limited
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/02664763.2020.1780570
dc.rightsArchived with thanks to Journal of Applied Statistics
dc.titleBirnbaum–Saunders sample selection model
dc.typeArticle
dc.contributor.departmentStatistics Program, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
dc.identifier.journalJournal of Applied Statistics
dc.eprint.versionPost-print
dc.contributor.institutionInstituto de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa - Campus UFV - Florestal, Florestal, Brazil
dc.contributor.institutionDepartamento de Estatística, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
dc.identifier.pages1-21
kaust.personBarreto-Souza, Wagner
dc.date.accepted2020-05-31
dc.identifier.eid2-s2.0-85087120461
refterms.dateFOA2020-07-08T10:40:57Z


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