Multivariate log-Birnbaum–Saunders regression models

References

• Arnold, B.C., Castilho, E., Sarabia, J.M. (2002). Conditionally specified multivariate skewed distributions. Sankhya A 64:206–226.
   Google Scholar
• Azzalini, A. (1985). A class of distributions which includes the normal ones. Scand. J. Stat. 12:171–178.
   Web of Science ®Google Scholar
• Birnbaum, Z., Saunders, S. (1969). A new family of life distributions. J. Appl. Probab. 6:319–327.
   Web of Science ®Google Scholar
• Castillo, N., Gomez, H., Bolfarine, H. (2011). Epsilon Birnbaum–Saunders distribution family: Properties and inference. Stat. Pap. 52:871–883.
   Web of Science ®Google Scholar
• Cook, R.D., Weisberg, S. (1983). Diagnostics for heteroscedasticity in regression. Biometrika 70:1–10.
   Web of Science ®Google Scholar
• Cordeiro, G.M., Lemonte, A.J. (2011). The beta-Birnbaum–Saunders distribution: An improved distribution for fatigue life modeling. Comput. Stat. Data Anal. 55:1445–1461.
   Web of Science ®Google Scholar
• Cordeiro, G.M., Lemonte, A.J. (2014). The exponentiated generalized Birnbaum–Saunders distribution. Appl. Math. Comput. 247:762–779.
   Web of Science ®Google Scholar
• Cordeiro, G.M., Lemonte, A.J., Ortega, E.M.M. (2013). An extended fatigue life distribution. Statistics 47:626–653.
   Web of Science ®Google Scholar
• Cox, D.R., Hinkley, D.V. (1974). Theoretical Statistics. London: Chapman and Hall.
   Google Scholar
• Díaz-García, J., Domínguez-Molina, J.R. (2006). Some generalisations of Birnbaum–Saunders and sinh-normal distributions. Int. Math. Forum 1:1709–1727.
   Google Scholar
• Doornik, J.A. (2006). An Object-Oriented Matrix Language - Ox 4. 5th ed. London: Timberlake Consultants Press.
   Google Scholar
• Farias, R.B.A., Lemonte, A.J. (2011). Bayesian inference for the Birnbaum–Saunders nonlinear regression model. Stat. Methods Appl. 20:423–438.
   Web of Science ®Google Scholar
• Ferrell, M.A. (2003). Effect of Water pH on the Chemical Stability of Pesticides. Series: Pesticide Education Program fact sheet, University of Wyoming.
   Google Scholar
• Galea, M., Leiva, V., Paula, G. (2004). Influence diagnostics in log-Birnbaum–Saunders regression models. J. Appl. Stat. 31:1049–1064.
   Web of Science ®Google Scholar
• Gomez, H., Olivares, J., Bolfarine, H. (2009). An extension of the generalized Birnbaum–Saunders distribution. Stat. Probab. Lett. 79:331–338.
   Web of Science ®Google Scholar
• Johnson, R.A., Wichern, D.W. (2007). Applied Multivariate Statistical Analysis, 6th ed. New Jersey: Prentice-Hall.
   Google Scholar
• Justel, A., Peña, D., Zamar, R. (1997). A multivariate Kolmogorov–Smirnov test of goodness of fit. Stat. Probab. Lett. 35(3):251–259.
   Web of Science ®Google Scholar
• Kundu, D., Balakrishnan, N., Jamalizadeh, A. (2010). Bivariate Birnbaum–Saunders distribution and associated inference. J. Multivariate Anal. 101:113–125.
   Web of Science ®Google Scholar
• Kundu, D. (2015). Bivariate sinh-normal distribution and a related model. Br. J. Probab. Stat. 29:590–607.
   Web of Science ®Google Scholar
• Leiva, V., Vilca-Labra, F., Balakrishnan, N., Sanhueza, A. (2010). A skewed sinh-normal distribution and its properties and application to air pollution. Commun. Stat.—Theory Methods 39:426–443.
   Web of Science ®Google Scholar
• Lemonte, A.J. (2013a). A new extension of the Birnbaum–Saunders distribution. Br. J. Probab. Stat. 27:133–149.
   Web of Science ®Google Scholar
• Lemonte, A.J. (2013b). A new extended Birnbaum–Saunders regression model for lifetime modeling. Comput. Stat. Data Anal. 64:34–50.
   Web of Science ®Google Scholar
• Lemonte, A. (2013c). Multivariate Birnbaum–Saunders regression model. J. Stat. Comput. Simul. 12:2244–2257.
   Web of Science ®Google Scholar
• Lemonte, A.J., Ferrari, S.L.P. (2011). Small-sample corrections for score tests in Birnbaum–Saunders regressions. Commun. Stat. - Theory Methods 40:232–243.
   Web of Science ®Google Scholar
• Lemonte, A., Martínez-Florez, G., Moreno-Arenas, G. (2015). Multivariate Birnbaum–Saunders distribution: Properties and associated inference. J. Stat. Comput. Simul. 85:374–392.
   Web of Science ®Google Scholar
• Lemonte, A.J., Patriota, A.G. (2011). Influence diagnostics in Birnbaum–Saunders nonlinear regression models. J. Appl. Stat. 38:871–884.
   Web of Science ®Google Scholar
• Nelsen, R.B. (1999). An Introduction to Copulas. New York: Springer.
   Google Scholar
• Owen, D.B. (1956). Tables for computing bivariate normal probabilities. Ann. Math. Stat. 27:1075–1090.
   Google Scholar
• Qu, H., Wei, B.C. (2011). Diagnostics analysis for log-Birnbaum–Saunders regression models with censored data. Stat. Neerlandica 65:1–21.
   Web of Science ®Google Scholar
• R Core Team., (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/
   Google Scholar
• Rieck, J.R., Nedelman, J.R. (1991). A log-linear model for the Birnbaum–Saunders distribution. Technometrics 33:51–60.
   Web of Science ®Google Scholar
• Tobias, P. (2004). Reliability. In: Group, S.M., ed. e-Handbook of Statistical Methods. NIST/SEMATECH. Available at http://www.itl.nist.gov/div898/handbook
   Google Scholar
• Vilca-Labra, F., Leiva-Sánchez, V. (2006). A new fatigue life model based on the family of skew-elliptical distributions. Commun. Stat.—Theory Methods 35:229–244.
   Web of Science ®Google Scholar
• Vuong, Q. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57:307–333.
   Web of Science ®Google Scholar
• Xie, F.C., Wei, B.C. (2007). Diagnostics analysis for log-Birnbaum–Saunders regression models. Comput. Stat. Data Anal. 51:4692–4706.
   Web of Science ®Google Scholar