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Author Barros, M.; Galea, M.; Leiva, V.; Santos-Neto, M. pdf  doi
openurl 
  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 Leiva, V.; Ferreira, M.; Gomes, M.I.; Lillo, C. pdf  doi
openurl 
  Title Extreme value Birnbaum-Saunders regression models applied to environmental data Type
  Year 2016 Publication Stochastic Environmental Research And Risk Assessment Abbreviated Journal Stoch. Environ. Res. Risk Assess.  
  Volume 30 Issue 3 Pages 1045-1058  
  Keywords Data analysis; Maximum likelihood method; Monte Carlo simulation; Residuals; Statistical modeling  
  Abstract Extreme value models are widely used in different areas. The Birnbaum-Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum-Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.  
  Address [Leiva, Victor] Univ Adolfo Ibanez, Fac Sci & Engn, Santiago, Chile, Email: victorleivasanchez@gmail.com  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1436-3240 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000371316900019 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 585  
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Author Santos-Neto, M.; Cysneiros, F.J.A.; Leiva, V.; Barros, M. pdf  doi
openurl 
  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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