| Records |
| Author |
Leao, J.; Leiva, V.; Saulo, H.; Tomazella, V. |
| Title |
Birnbaum-Saunders frailty regression models: Diagnostics and application to medical data |
Type |
|
| Year |
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 |
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Thesis |
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| Publisher |
Wiley |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0323-3847 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000396452500006 |
Approved |
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| Call Number |
UAI @ eduardo.moreno @ |
Serial |
707 |
| Permanent link to this record |
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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 |
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 |
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Thesis |
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| Publisher |
Elsevier Science Bv |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0167-9473 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000340139900013 |
Approved |
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| Call Number |
UAI @ eduardo.moreno @ |
Serial |
396 |
| Permanent link to this record |
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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 |
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Thesis |
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| Publisher |
Ieee-Inst Electrical Electronics Engineers Inc |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0018-9529 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000382706900027 |
Approved |
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| Call Number |
UAI @ eduardo.moreno @ |
Serial |
653 |
| Permanent link to this record |
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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 |
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Thesis |
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| Publisher |
Taylor & Francis Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0266-4763 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000384263000009 |
Approved |
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| Call Number |
UAI @ eduardo.moreno @ |
Serial |
662 |
| Permanent link to this record |
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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 |
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Thesis |
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| Publisher |
Inst Mathematical Statistics |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1935-7524 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000390364400036 |
Approved |
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| Call Number |
UAI @ eduardo.moreno @ |
Serial |
684 |
| Permanent link to this record |
