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Author Leiva, V.; Saulo, H.; Leao, J.; Marchant, C.
Title A family of autoregressive conditional duration models applied to financial data Type
Year (up) 2014 Publication Computational Statistics & Data Analysis Abbreviated Journal Comput. Stat. Data Anal.
Volume 79 Issue Pages 175-191
Keywords Birnbaum-Saunders distribution; EM algorithm; High-frequency data; Maximum likelihood estimator; Monte Carlo simulation
Abstract The Birnbaum-Saunders distribution is receiving considerable attention due to its good properties. One of its extensions is the class of scale-mixture Birnbaum-Saunders (SBS) distributions, which shares its good properties, but it also has further properties. The autoregressive conditional duration models are the primary family used for analyzing high-frequency financial data. We propose a methodology based on SBS autoregressive conditional duration models, which includes in-sample inference, goodness-of-fit and out-of-sample forecast techniques. We carry out a Monte Carlo study to evaluate its performance and assess its practical usefulness with real-world data of financial transactions from the New York stock exchange. (C) 2014 Elsevier B.V. All rights reserved.
Address [Leiva, Victor; Marchant, Carolina] Univ Valparaiso, Inst Estadist, Valparaiso, Chile, Email: victor.leiva@yahoo.com
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-9473 ISBN Medium
Area Expedition Conference
Notes WOS:000340139900013 Approved
Call Number UAI @ eduardo.moreno @ Serial 396
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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 (up) 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 (up) 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 Santos-Neto, M.; Cysneiros, F.J.A.; Leiva, V.; Barros, M.
Title Reparameterized Birnbaum-Saunders regression models with varying precision Type
Year (up) 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 Leao, J.; Leiva, V.; Saulo, H.; Tomazella, V.
Title Birnbaum-Saunders frailty regression models: Diagnostics and application to medical data Type
Year (up) 2017 Publication Biometrical Journal Abbreviated Journal Biom. J.
Volume 59 Issue 2 Pages 291-314
Keywords Birnbaum-Saunders distribution; Censored data; Global and local influence; Maximum-likelihood method; Residual analysis
Abstract In survival models, some covariates affecting the lifetime could not be observed or measured. These covariates may correspond to environmental or genetic factors and be considered as a random effect related to a frailty of the individuals explaining their survival times. We propose a methodology based on a Birnbaum-Saunders frailty regression model, which can be applied to censored or uncensored data. Maximum-likelihood methods are used to estimate the model parameters and to derive local influence techniques. Diagnostic tools are important in regression to detect anomalies, as departures from error assumptions and presence of outliers and influential cases. Normal curvatures for local influence under different perturbations are computed and two types of residuals are introduced. Two examples with uncensored and censored real-world data illustrate the proposed methodology. Comparison with classical frailty models is carried out in these examples, which shows the superiority of the proposed model.
Address [Leao, Jeremias] Univ Fed Amazonas, Dept Stat, Manaus, Amazonas, Brazil, Email: victorleivasanchez@gmail.com
Corporate Author Thesis
Publisher Wiley Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0323-3847 ISBN Medium
Area Expedition Conference
Notes WOS:000396452500006 Approved
Call Number UAI @ eduardo.moreno @ Serial 707
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