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Author Leiva, V.; Liu, S.Z.; Shi, L.; Cysneiros, F.J.A.
Title Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics Type
Year 2016 Publication Journal Of Applied Statistics Abbreviated Journal J. Appl. Stat.
Volume 43 Issue 4 Pages 627-642
Keywords computational statistics; elliptically contoured distributions; generalized least squares; local influence method; maximum-likelihood method; mixed estimation
Abstract We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.
Address [Leiva, Victor] Univ Adolfo Ibanez, Fac Sci & Engn, Vina Del Mar, Chile, 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:000368584400003 Approved
Call Number UAI @ eduardo.moreno @ Serial 575
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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 Marchant, C.; Leiva, V.; Cysneiros, F.J.A.
Title A Multivariate Log-Linear Model for Birnbaum-Saunders Distributions Type
Year 2016 Publication Ieee Transactions On Reliability Abbreviated Journal IEEE Trans. Reliab.
Volume 65 Issue 2 Pages 816-827
Keywords EM algorithm; fatigue data; logarithmic distributions; maximum likelihood method; Monte Carlo simulation; multivariate generalized Birnbaum-Saunders distributions; R software
Abstract Univariate Birnbaum-Saunders models have been widely applied to fatigue studies. Calculation of fatigue life is of great importance in determining the reliability of materials. We propose and derive new multivariate generalized Birnbaum-Saunders regression models. We use the maximum likelihood method and the EM algorithm to estimate their parameters. We carry out a simulation study to evaluate the performance of the corresponding maximum likelihood estimators. We illustrate the new models with real-world multivariate fatigue data.
Address [Marchant, Carolina] Univ Fed Pernambuco, Recife, PE, Brazil, Email: carolina.marchant.fuentes@gmail.com;
Corporate Author Thesis
Publisher Ieee-Inst Electrical Electronics Engineers Inc Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0018-9529 ISBN Medium
Area Expedition Conference
Notes WOS:000382706900027 Approved
Call Number UAI @ eduardo.moreno @ Serial 653
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Author Marchant, C.; Leiva, V.; Cysneiros, F.J.A.; Vivanco, J.F.
Title Diagnostics in multivariate generalized Birnbaum-Saunders regression models Type
Year 2016 Publication Journal Of Applied Statistics Abbreviated Journal J. Appl. Stat.
Volume 43 Issue 15 Pages 2829-2849
Keywords Birnbaum-Saunders distributions; global and local influence; goodness-of-fit; multivariate data analysis; R software
Abstract Birnbaum-Saunders (BS) models are receiving considerable attention in the literature. Multivariate regression models are a useful tool of the multivariate analysis, which takes into account the correlation between variables. Diagnostic analysis is an important aspect to be considered in the statistical modeling. In this paper, we formulate multivariate generalized BS regression models and carry out a diagnostic analysis for these models. We consider the Mahalanobis distance as a global influence measure to detect multivariate outliers and use it for evaluating the adequacy of the distributional assumption. We also consider the local influence approach and study how a perturbation may impact on the estimation of model parameters. We implement the obtained results in the R software, which are illustrated with real-world multivariate data to show their potential applications.
Address [Marchant, Carolina; Cysneiros, Francisco Jose A.] Univ Fed Pernambuco, Dept Stat, Recife, PE, 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:000384263000009 Approved
Call Number UAI @ eduardo.moreno @ Serial 662
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Author Sanchez, L.; Leiva, V.; Caro-Lopera, F.J.; Cysneiros, F.J.A.
Title On matrix-variate Birnbaum-Saunders distributions and their estimation and application Type
Year 2015 Publication Brazilian Journal Of Probability And Statistics Abbreviated Journal Braz. J. Probab. Stat.
Volume 29 Issue 4 Pages 790-812
Keywords Computer language; data analysis; elliptically contoured distribution; maximum likelihood estimator; Monte Carlo method; shape theory
Abstract Diverse phenomena from the real-world can be modeled using random matrices, allowing matrix-variate distributions to be considered. The normal distribution is often employed in this modeling, but usually the mentioned random matrices do not follow such a distribution. An asymmetric non-normal model that is receiving considerable attention due to its good properties is the Birnbaum-Saunders (BS) distribution. We propose a statistical methodology based on matrix-variate BS distributions. This methodology is implemented in the statistical software R. A simulation study is conducted to evaluate its performance. Finally, an application with real-world matrix-variate data is carried out to illustrate its potentiality and suitability.
Address [Sanchez, Luis] Univ Valparaiso, Inst Estadist, Valparaiso, Chile, Email: ldaniel9.24@gmail.com;
Corporate Author Thesis
Publisher Brazilian Statistical Association Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0103-0752 ISBN Medium
Area Expedition Conference
Notes WOS:000362310900005 Approved
Call Number UAI @ eduardo.moreno @ Serial 621
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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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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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