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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 Garcia-Papani, F.; Uribe-Opazo, M.A.; Leiva, V.; Aykroyd, R.G.
Title Birnbaum-Saunders spatial modelling and diagnostics applied to agricultural engineering data Type
Year 2017 Publication Stochastic Environmental Research And Risk Assessment Abbreviated Journal Stoch. Environ. Res. Risk Assess.
Volume 31 Issue 1 Pages 105-124
Keywords Asymmetric distributions; Local influence; Matern model; Maximum likelihood methods; Monte Carlo simulation; Non-normality; R software; Spatial data analysis
Abstract Applications of statistical models to describe spatial dependence in geo-referenced data are widespread across many disciplines including the environmental sciences. Most of these applications assume that the data follow a Gaussian distribution. However, in many of them the normality assumption, and even a more general assumption of symmetry, are not appropriate. In non-spatial applications, where the data are uni-modal and positively skewed, the Birnbaum-Saunders (BS) distribution has excelled. This paper proposes a spatial log-linear model based on the BS distribution. Model parameters are estimated using the maximum likelihood method. Local influence diagnostics are derived to assess the sensitivity of the estimators to perturbations in the response variable. As illustration, the proposed model and its diagnostics are used to analyse a real-world agricultural data set, where the spatial variability of phosphorus concentration in the soil is considered-which is extremely important for agricultural management.
Address [Garcia-Papani, Fabiana; Uribe-Opazo, Miguel Angel] Univ Estadual Oeste Parana, Postgrad Program Agr Engn, Ctr Exact Sci & Technol, Cascavel, PR, Brazil, Email: fgarciapapani@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:000394278600008 Approved
Call Number UAI @ eduardo.moreno @ Serial 704
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Author Leiva, V.; Ferreira, M.; Gomes, M.I.; Lillo, C.
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 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 Leiva, V.; Tejo, M.; Guiraud, P.; Schmachtenberg, O.; Orio, P.; Marmolejo-Ramos, F.
Title Modeling neural activity with cumulative damage distributions Type
Year 2015 Publication Biological Cybernetics Abbreviated Journal Biol. Cybern.
Volume 109 Issue 4-5 Pages 421-433
Keywords Birnbaum-Saunders and inverse Gaussian distributions; Integrate-and-fire model; Inter-spike intervals; Maximum likelihood method; Model selection and goodness of fit
Abstract Neurons transmit information as action potentials or spikes. Due to the inherent randomness of the inter-spike intervals (ISIs), probabilistic models are often used for their description. Cumulative damage (CD) distributions are a family of probabilistic models that has been widely considered for describing time-related cumulative processes. This family allows us to consider certain deterministic principles for modeling ISIs from a probabilistic viewpoint and to link its parameters to values with biological interpretation. The CD family includes the Birnbaum-Saunders and inverse Gaussian distributions, which possess distinctive properties and theoretical arguments useful for ISI description. We expand the use of CD distributions to the modeling of neural spiking behavior, mainly by testing the suitability of the Birnbaum-Saunders distribution, which has not been studied in the setting of neural activity. We validate this expansion with original experimental and simulated electrophysiological data.
Address [Leiva, Victor] Univ Adolfo Ibanez, Fac Sci & Engn, Vina Del Mar, 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 0340-1200 ISBN Medium
Area Expedition Conference
Notes WOS:000361484300001 Approved
Call Number UAI @ eduardo.moreno @ Serial 528
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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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