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Author Barros, M.; Galea, M.; Leiva, V.; Santos-Neto, M.
Title Generalized Tobit models: diagnostics and application in econometrics Type
Year 2018 Publication Journal Of Applied Statistics Abbreviated Journal J. Appl. Stat.
Volume 45 Issue 1 Pages 145-167
Keywords Cook distance; elliptically contoured distributions; labor supply data; local influence method; maximum likelihood method; R software; residuals; Student-t distribution
Abstract The standard Tobit model is constructed under the assumption of a normal distribution and has been widely applied in econometrics. Atypical/extreme data have a harmful effect on the maximum likelihood estimates of the standard Tobit model parameters. Then, we need to count with diagnostic tools to evaluate the effect of extreme data. If they are detected, we must have available a Tobit model that is robust to this type of data. The family of elliptically contoured distributions has the Laplace, logistic, normal and Student-t cases as some of its members. This family has been largely used for providing generalizations of models based on the normal distribution, with excellent practical results. In particular, because the Student-t distribution has an additional parameter, we can adjust the kurtosis of the data, providing robust estimates against extreme data. We propose a methodology based on a generalization of the standard Tobit model with errors following elliptical distributions. Diagnostics in the Tobit model with elliptical errors are developed. We derive residuals and global/local influence methods considering several perturbation schemes. This is important because different diagnostic methods can detect different atypical data. We implement the proposed methodology in an R package. We illustrate the methodology with real-world econometrical data by using the R package, which shows its potential applications. The Tobit model based on the Student-t distribution with a small quantity of degrees of freedom displays an excellent performance reducing the influence of extreme cases in the maximum likelihood estimates in the application presented. It provides new empirical evidence on the capabilities of the Student-t distribution for accommodation of atypical data.
Address [Barros, Michelli; Santos-Neto, Manoel] Univ Fed Campina Grande, Dept Stat, Campina Grande, Brazil, Email: victorleivasanchez@gmail.com
Corporate Author Thesis
Publisher Taylor & Francis Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0266-4763 ISBN Medium
Area Expedition Conference
Notes WOS:000415929600011 Approved
Call Number UAI @ eduardo.moreno @ Serial 781
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Author Santos-Neto, M.; Cysneiros, F.J.A.; Leiva, V.; Barros, M.
Title Reparameterized Birnbaum-Saunders regression models with varying precision Type
Year 2016 Publication Electronic Journal Of Statistics Abbreviated Journal Electron. J. Stat.
Volume 10 Issue 2 Pages 2825-2855
Keywords Birnbaum-Saunders distribution; hypothesis testing; likelihood-based methods; local influence; Monte Carlo simulation; residuals; R software
Abstract We propose a methodology based on a reparameterized Birnbaum-Saunders regression model with varying precision, which generalizes the existing works in the literature on the topic. This methodology includes the estimation of model parameters, hypothesis tests for the precision parameter, a residual analysis and influence diagnostic tools. Simulation studies are conducted to evaluate its performance. We apply it to two real-world case-studies to show its potential with the R software.
Address [Santos-Neto, Manoel; Barros, Michelli] Univ Fed Campina Grande, Dept Stat, Campina Grande, Brazil, Email: manoel.ferreira@ufcg.edu.br;
Corporate Author Thesis
Publisher Inst Mathematical Statistics Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1935-7524 ISBN Medium
Area Expedition Conference
Notes WOS:000390364400036 Approved
Call Number UAI @ eduardo.moreno @ Serial 684
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Author Leiva, V.; Santos-Neto, M.; Cysneiros, F.J.A.; Barros, M.
Title A methodology for stochastic inventory models based on a zero-adjusted Birnbaum-Saunders distribution Type
Year 2016 Publication Applied Stochastic Models In Business And Industry Abbreviated Journal Appl. Stoch. Models. Bus. Ind.
Volume 32 Issue 1 Pages 74-89
Keywords demand data; financial indicators; maximum likelihood method; mixture distributions; Monte Carlo simulation; R software
Abstract The Birnbaum-Saunders (BS) distribution is receiving considerable attention. We propose a methodology for inventory logistics that allows demand data with zeros to be modeled by means of a new discrete-continuous mixture distribution, which is constructed by using a probability mass at zero and a continuous component related to the BS distribution. We obtain some properties of the new mixture distribution and conduct a simulation study to evaluate the performance of the estimators of its parameters. The methodology for stochastic inventory models considers also financial indicators. We illustrate the proposed methodology with two real-world demand data sets. It shows its potential, highlighting the convenience of using it by improving the contribution margins of a Chilean food industry. Copyright (c) 2015 John Wiley & Sons, Ltd.
Address [Leiva, Victor] Univ Valparaiso, Inst Stat, Valparaiso, Chile, Email: victorleivasanchez@gmail.com
Corporate Author Thesis
Publisher Wiley-Blackwell Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1524-1904 ISBN Medium
Area Expedition Conference
Notes WOS:000369134600006 Approved
Call Number UAI @ eduardo.moreno @ Serial 580
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Author Santos-Neto, M.; Cysneiros, F.J.A.; Leiva, V.; Barros, M.
Title A Reparameterized Birnbaum-Saunders Distribution And Its Moments, Estimation And Applications Type
Year 2014 Publication REVSTAT-Statistical Journal Abbreviated Journal REVSTAT-Stat. J.
Volume 12 Issue 3 Pages 247-272
Keywords data analysis; maximum likelihood and moment estimation; Monte Carlo method; random number generation; statistical software
Abstract The Birnbaum-Saunders (BS) distribution is a model that is receiving considerable attention due to its good properties. We provide some results on moments of a reparameterized version of the BS distribution and a generation method of random numbers from this distribution. In addition, we propose estimation and inference for the mentioned parameterization based on maximum likelihood, moment, modified moment and generalized moment methods. By means of a Monte Carlo simulation study, we evaluate the performance of the proposed estimators. We discuss applications of the reparameterized BS distribution from different scientific fields and analyze two real-world data sets to illustrate our results. The simulated and real data are analyzed by using the R software.
Address [Santos-Neto, Manoel; Barros, Michelli] Univ Fed Campina Grande, Dept Estat, Campina Grande, Brazil, Email: manoel.ferreira@ufcg.edu.br;
Corporate Author Thesis
Publisher Inst Nacional Estatistica-Ine Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1645-6726 ISBN Medium
Area Expedition Conference
Notes WOS:000349017700003 Approved
Call Number UAI @ eduardo.moreno @ Serial 448
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