Electronic Journal of Statistics

Reparameterized Birnbaum-Saunders regression models with varying precision

Manoel Santos-Neto, Francisco José A. Cysneiros, Víctor Leiva, and Michelli Barros

Full-text: Open access

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.

Article information

Source
Electron. J. Statist. Volume 10, Number 2 (2016), 2825-2855.

Dates
Received: July 2014
First available in Project Euclid: 30 September 2016

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1475266648

Digital Object Identifier
doi:10.1214/16-EJS1187

Zentralblatt MATH identifier
1348.62220

Subjects
Primary: 62J12: Generalized linear models 62J20: Diagnostics
Secondary: 62F03: Hypothesis testing

Keywords
Birnbaum-Saunders distribution hypothesis testing likelihood-based methods local influence Monte Carlo simulation residuals R software

Citation

Santos-Neto, Manoel; Cysneiros, Francisco José A.; Leiva, Víctor; Barros, Michelli. Reparameterized Birnbaum-Saunders regression models with varying precision. Electron. J. Statist. 10 (2016), no. 2, 2825--2855. doi:10.1214/16-EJS1187. https://projecteuclid.org/euclid.ejs/1475266648


Export citation

References

  • [1] Bhatti, C. (2010). The Birnbaum-Saunders autoregressive conditional duration, model.Mathematics and Computers in Simulation,802062–2078.doi:10.1016/j.matcom.2010.01.011.
  • [2] Birnbaum, Z. W. and Saunders, S. C. (1969). A new family of life, distributions.Journal of Applied Probability,6319–327.doi:10.2307/3212003.
  • [3] Cook, R. D. and Weisberg, S. (1983). Diagnostics for heteroscedasticity in, regression.Biometrika,701–10.doi:10.2307/2335938.
  • [4] Cox, D. and Hinkley, D., (1974).Theoretical Statistics. Chapman and Hall, London, UK.
  • [5] Cysneiros, F., Paula, G., and Galea, M. (2007). Heteroscedastic symmetrical linear, models.Statistical and Probability Letters,771084–1090.doi:10.1016/j.spl.2007.01.012.
  • [6] Dunn, P. and Smyth, G. (1996). Randomized quantile, residuals.Journal of Computational and Graphical Statistics,5236–244.doi:10.2307/1390802
  • [7] Ferrari, S., Espinheira, P., and Cribari-Neto, F. (2011). Diagnostic tools in beta regression with varying, dispersion.Statistica Neerlandica,65337–351.doi:10.1111/j.1467-9574.2011.00488.x.
  • [8] Ferreira, M., Gomes, M. I., and Leiva, V. (2012). On an extreme value version of the Birnbaum-Saunders, distribution.REVSTAT Statistical Journal,10181–210.https://www.ine.pt/revstat/pdf/rs120202.pdf.
  • [9] Galea, M., Leiva, V., and Paula, G. (2004). Influence diagnostics in log-Birnbaum-Saunders regression, models.Journal of Applied Statistics,311049–1064.doi:10.1080/0266476042000280409.
  • [10] Garcia-Papani, F., Uribe-Opazo, M., Leiva, V., and Aykroyd, R. (2016). Birnbaum-Saunders spatial modelling and diagnostics applied to agricultural engineering, data.Stochastic Environmental Research and Risk Assessment, pages in press available atdoi:10.1007/s00477-015-1204-4.
  • [11] Jin, X. and Kawczak, J. (2003). Birnbaum-Saunders and lognormal kernel estimators for modelling durations in high frequency financial, data.Annals of Economics and Finance,4103–124.http://aeconf.com/Articles/May2003/aef040106.pdf.
  • [12] Johnson, N., Kotz, S., and Balakrishnan, N., (1995).Continuous Univariate Distributions, volume 2. Wiley, New York, US.
  • [13] Leiva, V., (2016).The Birnbaum-Saunders Distribution. Academic Press, New York, US.
  • [14] Leiva, V., Marchant, C., Ruggeri, F., and Saulo, H. (2015a). A criterion for environmental assessment using Birnbaum-Saunders attribute control, charts.Environmetrics,26463–476.doi:10.1002/env.2349.
  • [15] Leiva, V., Rojas, E., Galea, M., and Sanhueza, A. (2014a). Diagnostics in Birnbaum-Saunders accelerated life models with an application to fatigue, data.Applied Stochastic Models in Business and Industry,30115–131.doi:10.1002/asmb.1944.
  • [16] Leiva, V., Santos-Neto, M., Cysneiros, F. J. A., and Barros, M. (2014b). Birnbaum-Saunders statistical modelling: A new, approach.Statistical Modelling,1421–48.doi:10.1177/1471082X13494532.
  • [17] Leiva, V., Santos-Neto, M., Cysneiros, F. J. A., and Barros, M. (2016). A methodology for stochastic inventory models based on a zero-adjusted Birnbaum-Saunders, distribution.Applied Stochastic Models in Business and Industry,3274–89.doi:10.1002/asmb.2124.
  • [18] Leiva, V., Saulo, H., Leão, J., and Marchant, C. (2014c). A family of autoregressive conditional duration models applied to financial, data.Computational Statistics and Data Analysis,79175–191.doi:10.1016/j.csda.2014.05.016.
  • [19] Leiva, V., Tejo, M., Guiraud, P., Schmachtenberg, O., Orio, P., and Marmolejo, F. (2015b). Modeling neural activity with cumulative damage, distributions.Biological Cybernetics,109421–433.doi:10.1007/s00422-015-0651-9.
  • [20] Li, A., Chen, Z., and Xie, F. (2012). Diagnostic analysis for heterogeneous log-Birnbaum-Saunders regression, models.Statistical and Probability Letters,891690–1698.doi:10.1016/j.spl.2012.05.021.
  • [21] Lin, J., Zhu, L., and Xie, F. (2009). Heteroscedasticity diagnostics for $t$ linear regression, models.Metrika,7059–77.doi:10.1007/s00184-008-0179-2.
  • [22] Marchant, C., Leiva, V., and Cysneiros, F. (2016a). A multivariate log-linear model for Birnbaum-Saunders, distributions.IEEE Transactions on Reliability,65816–827doi:10.1109/TR.2015.2499964.
  • [23] Marchant, C., Leiva, V., Cysneiros, F., and Vivanco, J. (2016b). Diagnostics in multivariate generalized Birnbaum-Saunders regression, models.Journal of Applied Statistics,432829–2849doi:10.1080/02664763.2016.1148671.
  • [24] Owen, W. (2006). A new three-parameter extension to the Birnbaum-Saunders, distribution.IEEE Transactions on Reliability,55475–479.doi:10.1109/TR.2006.879646.
  • [25] Owen, W. and Padgett, W. (2000). A Birnbaum-Saunderss accelerated life, model.IEEE Transactions on Reliability,49224–229.doi:10.1109/24.877342.
  • [26] Paula, G. A. (2013). On diagnostics in double generalized linear, models.Computational Statistics and Data Analysis,6844–51.doi:10.1016/j.csda.2013.06.008.
  • [27] Paula, G. A., Leiva, V., Barros, M., and Liu, S. (2012). Robust statistical modeling using the Birnbaum-Saunders-t distribution applied to, insurance.Applied Stochastic Models in Business and Industry,2816–34.doi:10.1002/asmb.887.
  • [28] Qu, H. and Xie, F. (2011). Diagnostics analysis for log-Birnbaum-Saunders regression models with censored, data.Statistica Neerlandica,651–21.doi:10.1111/j.1467-9574.2010.00467.x.
  • [29] R-Team, (2016).R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.https://cran.r-project.org/doc/manuals/r-release/fullrefman.pdf.
  • [30] Rieck, J. and Nedelman, J. (1991). A log-linear model for the Birnbaum-Saunders, distribution.Technometrics,351–60.http://www.jstor.org/stable/1269007.
  • [31] Rocha, A. V. and Simas, A. B. (2011). Influence diagnostic in a general class of beta regression, models.TEST,2095–119.doi:10.1007/s11749-010-0189-z.
  • [32] Rojas, F., Leiva, V., Wanke, P., and Marchant, C. (2015). Optimization of contribution margins in food services by modeling independent component, demand.Revista Colombiana de Estadística,381–30.doi:10.15446/rce.v38n1.48799.
  • [33] Santos-Neto, M., Cysneiros, F., Leiva, V., and Barros, M., (2016).RBS: Reparameterized Birnbaum-Saunders regression model. R package version 0.0.1https://github.com/santosneto/RBS.
  • [34] Santos-Neto, M., Cysneiros, F. J. A., Leiva, V., and Ahmed, S. (2012). On new parameterizations of the Birnbaum-Saunders, distribution.Pakistan Journal of Statistics,281–26.
  • [35] Santos-Neto, M., Cysneiros, F. J. A., Leiva, V., and Barros, M. (2014). On new parameterizations of the Birnbaum-Saunders distribution and its moments, estimation and, application.REVSTAT Statistical Journal,12247–272.https://www.ine.pt/revstat/pdf/rs140303.pdf.
  • [36] Saulo, H., Leiva, V., Ziegelmann, F. A., and Marchant, C. (2013). A nonparametric method for estimating asymmetric densities based on skewed Birnbaum-Saunders distributions applied to environmental, data.Stochastic Environmental Research and Risk Assessment,271479–1491.doi:10.1007/s00477-012-0684-8
  • [37] Saumard, A. (2013). Optimal model selection in heteroscedastic regression using piecewise polynomial, functions.Electronic Journal of Statistics,71184–1223.doi:10.1214/13-EJS803.
  • [38] Simas, A. B., Barreto-Souza, W., and Rocha, A. V. (2010). Improved estimators for a general class of beta regression, models.Computational Statistics and Data Analysis,54348–366.doi:10.1016/j.csda.2009.08.017.
  • [39] Smyth, G. (1989). Generalized linear models with varying, dispersion.Journal of the Royal Statistical Society B,5147–60.http://www.jstor.org/stable/2345840.
  • [41] Stasinopoulos, D. and Rigby, R. (2007). Generalized additive models for location, scale and shape, (GAMLSS).Journal of Statistical Software,231–46.doi:10.18637/jss.v023.i07.
  • [42] Taylor, J. and Verbyla, A. (2004). Joint modeling of location and scale parameters of the t, distribution.Statistical Modelling,491–112.doi:10.1191/1471082X04st068oa.
  • [43] Van Keilegom, I. and Wang, L. (2010). Semiparametric modeling and estimation of heteroscedasticity in regression analysis of cross-sectional, data.Electronic Journal of Statistics,4133–160.doi:10.1214/09-EJS547.
  • [44] Vanegas, L., Rondon, L., and Cysneiros, F. (2012). Diagnostic procedures in Birnbaum-Saunders nonlinear regression, models.Computational Statistics and Data Analysis,561662–1680.doi:10.1016/j.csda.2011.10.008.
  • [45] Venezuela, M. K. and Artes, R. (2014). Estimating equations and diagnostic techniques applied to zero-inflated models for panel, data.Electronic Journal of Statistics,81641–1660.doi:10.1214/14-EJS936.
  • [46] Villegas, C., Paula, G., and Leiva, V. (2011). Birnbaum-Saunders mixed models for censored reliability data, analysis.IEEE Transactions on Reliability,60748–758.doi:10.1109/TR.2011.2170251.
  • [47] Wanke, P. and Leiva, V. (2015). Exploring the potential use of the Birnbaum-Saunders distribution in inventory, management.Mathematical Problems in Engineering, Article ID 827246:1–9.doi:10.1155/2015/827246.
  • [48] Weisberg, S., (2005).Applied Linear Regression. Wiley, New York, US.doi:10.1002/0471704091.
  • [49] Wu, L., Zhang, Z., and Xu, D. (2012). Variable selection for joint mean and dispersion models of the lognormal, distribution.Hacettepe Journal of Mathematics and Statistics,41307–320.http://www.hjms.hacettepe.edu.tr/uploads/b6d36a93-bbda-4e78-a4a7-940cc4d2e31f.pdf.
  • [50] Xie, F. and Wei, B. (2007). Diagnostics analysis for log-Birnbaum-Saunders regression, models.Computational Statistics and Data Analysis,514692–4706.doi:10.1016/j.csda.2006.08.030.