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Extreme value Birnbaum–Saunders regression models applied to environmental data

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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.

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References

  • Athayde E, Azevedo C, Leiva V, Sanhueza A (2012) About Birnbaum–Saunders distributions based on the Johnson system. Commun Stat Theory Methods 41:2061–2079

    Article  Google Scholar 

  • Atkinson AC (1985) Plots, transformations, and regression: an introduction to graphical methods of diagnostic regression analysis. Clarendon Press, Oxford

    Google Scholar 

  • Balkema AA, de Haan L (1974) Residual life time at great age. Ann Probab 2:227–247

    Article  Google Scholar 

  • Barros M, Leiva V, Ospina R, Tsuyuguchi A (2014) Goodness-of-fit tests for the Birnbaum–Saunders distribution with censored reliability data. IEEE Trans Reliab 63:543–554

    Article  Google Scholar 

  • Beirlant J, Caeiro F, Gomes MI (2012) An overview and open research topics in statistics of univariate extremes. Revstat Stat J 10:1–31

    Google Scholar 

  • Byrd R, Lu P, Nocedal J, Zhu C (1995) A limited memory algorithm for bound constrained optimization. SIAM J Sci Comput 16:1190–1208

    Article  Google Scholar 

  • Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London

    Book  Google Scholar 

  • Cox DR, Hinkley DV (1974) Theoretical statistics. Chapman and Hall, London

    Book  Google Scholar 

  • Cox DR, Snell EJ (1968) A general definition of residuals. J R Stat Soc B 2:248–275

    Google Scholar 

  • Crawley M (2007) The R book. Wiley, New York

    Book  Google Scholar 

  • de Haan L, Ferreira A (2006) Extreme value theory: an introduction. Springer, New York

    Book  Google Scholar 

  • Dobson AJ (2001) An introduction to generalized linear models. Chapman and Hall, New York

    Book  Google Scholar 

  • Dunn P, Smyth G (1996) Randomized quantile residuals. Comput Graph Stat 5:236–244

    Google Scholar 

  • Embrechts P, Klüppelberg C, Mikosch T (1997) Modelling extremal events for insurance and finance. Springer, Berlin

    Book  Google Scholar 

  • Ferreira M, Gomes MI, Leiva V (2012) On an extreme value version of the Birnbaum–Saunders distribution. Revstat Stat J 10:181–210

    Google Scholar 

  • Galea M, Paula GA, Leiva V (2004) Influence diagnostics in log-Birnbaum–Saunders regression models. J Appl Stat 31:1049–1064

    Article  Google Scholar 

  • Gnedenko BV (1943) Sur la distribution limite du terme maximum d’une série aléatoire. Ann Math 44:423–453

    Article  Google Scholar 

  • Gnedenko BV, Korolev VY (1996) Random summation: limit theorems and applications. CRC-Press, Boca Raton

    Google Scholar 

  • Gomes MI, Ferreira M, Leiva V (2012) The extreme value Birnbaum–Saunders model, its moments and an application in biometry. Biom Lett 49:81–94

    Google Scholar 

  • Johnson N, Kotz S, Balakrishnan N (1995) Continuous univariate distributions, vol 2. Wiley, New York

    Google Scholar 

  • Kelley C (1999) Iterative methods for optimization. SIAM, Philadelphia

    Book  Google Scholar 

  • Kim D, Kim B, Lee S, Cho Y (2014) Best distribution and log-normality for tsunami heights along coastal lines. Stoch Environ Res Risk Assess 28:881–893

    Article  Google Scholar 

  • Kotz S, Leiva V, Sanhueza A (2010) Two new mixture models related to the inverse Gaussian distribution. Methodol Comput Appl Probab 12:199–212

    Article  Google Scholar 

  • Leadbetter MR, Lindgren G, Rootzén H (1983) Extremes and related properties of random sequences and series. Springer, New York

    Book  Google Scholar 

  • Leiva V, Sanhueza A, Sen PK (2008 ) Random number generators for the generalized Birnbaum–Saunders distribution. J Stat Comput Simul 78:1105–1118

    Article  Google Scholar 

  • Leiva V, Sanhueza A, Angulo JM (2009) A length-biased version of the Birnbaum–Saunders distribution with application in water quality. Stoch Environ Res Risk Assess 23:299–307

    Article  Google Scholar 

  • Leiva V, Athayde E, Azevedo C, Marchant C (2011) Modeling wind energy flux by a Birnbaum–Saunders distribution with unknown shift parameter. J Appl Stat 38:2819–2838

    Article  Google Scholar 

  • Leiva V, Ponce MG, Marchant C, Bustos O (2012) Fatigue statistical distributions useful for modeling diameter and mortality of trees. Colomb J Stat 35:349–367

    Google Scholar 

  • Leiva V, Marchant C, Saulo H, Aslam M, Rojas F (2014a) Capability indices for Birnbaum–Saunders processes applied to electronic and food industries. J Appl Stat 41:1881–1902

    Article  Google Scholar 

  • Leiva V, Rojas E, Galea M, Sanhueza A (2014b) Diagnostics in Birnbaum–Saunders accelerated life models with an application to fatigue data. Appl Stoch Models Bus Ind 30:115–131

    Article  Google Scholar 

  • Leiva V, Santos-Neto M, Cysneiros FJA, Barros M (2014c) Birnbaum–Saunders statistical modelling: a new approach. Stat Model 14:21–48

    Article  Google Scholar 

  • Leiva V, Saulo H, Leao J, Marchant C (2014d) A family of autoregressive conditional duration models applied to financial data. Comput Stat Data Anal 79:175–191

    Article  Google Scholar 

  • Marchant C, Bertin K, Leiva V, Saulo H (2013) Generalized Birnbaum–Saunders kernel density estimators and an analysis of financial data. Comput Stat Data Anal 63:1–15

    Article  Google Scholar 

  • Marchant C, Leiva V, Cavieres MF, Sanhueza A (2013) Air contaminant statistical distributions with application to PM10 in Santiago, Chile. Rev Environ Contam Toxicol 223:1–31

    CAS  Google Scholar 

  • Marshall AW, Olkin I (2007) Life distributions. Springer, New York

    Google Scholar 

  • Paula GA, Leiva V, Barros M, Liu S (2012) Robust statistical modeling using the Birnbaum–Saunders-\(t\) distribution applied to insurance. Appl Stoch Models Bus Ind 28:16–34

    Article  Google Scholar 

  • Pickands J (1975) Statistical inference using extreme order statistics. Ann Stat 3:119–131

    Article  Google Scholar 

  • Rachev ST, Resnick S (1991) Max-geometric infinite divisibility and stability. Commun Stat Stoch Models 7:191–218

    Article  Google Scholar 

  • Rieck JR, Nedelman JR (1991) A log-linear model for the Birnbaum–Saunders distribution. Technometrics 33:51–60

    Google Scholar 

  • Santana L, Vilca F, Leiva V (2011) Influence analysis in skew-Birnbaum–Saunders regression models and applications. J Appl Stat 38:1633–1649

    Article  Google Scholar 

  • Saulo H, Leiva V, Ziegelmann FA, Marchant C (2013) A nonparametric method for estimating asymmetric densities based on skewed Birnbaum–Saunders distributions applied to environmental data. Stoch Environ ResK Risk Assess 27:1479–1491

    Article  Google Scholar 

  • Vanegas LH, Rondon LM, Cysneiros FJA (2012) Diagnostic procedures in Birnbaum–Saunders nonlinear regression models. Comput Stat Data Anal 56:1662–1680

    Article  Google Scholar 

  • Vilca F, Sanhueza A, Leiva V, Christakos G (2010) An extended Birnbaum–Saunders model and its application in the study of environmental quality in Santiago, Chile. Stoch Environ Res Risk Assess 24:771–782

    Article  Google Scholar 

  • Villegas C, Paula GA, Leiva V (2011) Birnbaum–Saunders mixed models for censored reliability data analysis. IEEE Trans Reliab 60:748–758

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to thank the Editors and three anonymous referees for their constructive comments on an earlier version of this manuscript, which resulted in this improved version. This study was partially supported by the Chilean Council for Scientific and Technological Research under the project grant FONDECYT 1120879, by FEDER Funds through “Programa Operacional de Factores de Competitividade-COMPETE” and by Portuguese Funds through “Fundação para a Ciência e a Tecnologia” (FCT) under the project Grants PEst-OE/MAT/UI0006/2014 (CEAUL) and PEst-OE/MAT/UI0013/2014.

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Correspondence to Víctor Leiva.

Appendices

Appendix 1: Hessian matrix

For the case \(\xi \ne 0\), \({\varvec{V}} = {{\text{ diag }}(v_1,\ldots ,v_n)}\), \({\varvec{k}} = (k_1,\ldots ,k_n)^\top \) and \({\varvec{u}} = (u_1,\ldots ,u_n)^\top \) defined in (7) have elements

$$\begin{aligned} v_i= & {} -\frac{\text { sech}^2(\zeta _{i1})(1+\zeta _{i2}\xi )^{{{-2}}-\frac{1}{\xi }}}{8\alpha ^2} \left( 3+4\xi +4\cosh (2\zeta _{i1})(1+\xi )\right. \\&\left. -\cosh (4\zeta _{i1}) +\frac{\alpha ^2}{2}\zeta _{i2}+\alpha \sinh (3\zeta _{i1})\right. \\&\left. +(1+\zeta _{i2}\xi )^{\frac{1}{\xi }} \big (2(\alpha ^2+2\xi )+4\xi (1+2\xi )\cosh (2\zeta _{i1}) +\frac{\alpha ^2\zeta _{i2}(1-7\xi )}{2}\big .\right. \\&\left. \left. -\alpha (1+\xi )\sinh (3\zeta _{i1})\right) \right) ,\\ k_i= & {} \displaystyle \frac{\cosh (\zeta _{i1})}{\alpha ^2}(1+\zeta _{i2}\xi )^{-2-\frac{1}{\xi }} \left( 1-\zeta _{i2}-(1+\xi )(1+\xi \zeta _{i2})^{\frac{1}{\xi }}\right) ,\\ u_i= & {} \displaystyle \frac{\cosh (\zeta _{i1})(1+\zeta _{i2}\xi )^{-2-\frac{1}{\xi }}}{\xi ^2\alpha } \Big (2\xi -\xi \log (1+\zeta _{i2}\xi )-\,\log (1+\zeta _{i2}\xi )\zeta _{i2}\Big .\\&\Big . +\left( -\xi +(\xi -1)\log (1+\zeta _{i2}\xi )-\,\xi \zeta _{i2}\right) (1+\zeta _{i2}\xi )^{\frac{1}{\xi }}\Big ). \end{aligned}$$

In addition, we have

$$\begin{aligned} \displaystyle \ddot{\ell }_{\alpha \alpha }\,=\, & {} \displaystyle \frac{n}{\alpha ^2}+{\sum _{i=1}^{n}} \frac{2\zeta _{i2}(\xi -1+(1+\zeta _{i2}\xi )^{-\frac{1}{\xi }})}{{(1+\zeta _{i2}\xi )}}\\&+{\sum _{i=1}^{n}}\frac{2(1+\xi )}{\alpha ^4}(\cosh (2\zeta _{i1})-1)(1+\zeta _{i2}\xi )^{-2-\frac{1}{\xi }}(\xi (1+\zeta _{i2}\xi )^{\frac{1}{\xi }}-1),\\ \displaystyle \ddot{\ell }_{\alpha \xi }\,=\,& {} \displaystyle \ddot{\ell }_{\xi \alpha }\,=\, \displaystyle {\sum _{i=1}^{n}} \frac{\zeta _{i2}(1+\zeta _{i2}\xi )^{-2-\frac{1}{\xi }}}{\alpha \xi ^2} \Big (\log (1+\zeta _{i2}\xi )+\xi \Big ((1+\xi -\log (1+\zeta _{i2}\xi ))\zeta _{i2} \Big .\Big .,\\&\Big .\Big .\displaystyle +\xi (1-\zeta _{i2})(1+\zeta _{i2}\xi )^{\frac{1}{\xi }}\Big )\Big ),\\ {\displaystyle \ddot{\ell }_{\xi \xi }}= & {} \displaystyle {\sum _{i=1}^{n}} \frac{(1+\zeta _{i2}\xi )^{-2-\frac{1}{\xi }}}{\xi ^4\alpha ^2} \Big (\alpha ^{2}\left( 2\xi - \log (1+\zeta _{i2}\xi )\right) \log (1+\zeta _{i2}\xi ) -2\alpha ^{2}\xi \left( 1+\zeta _{i2}\xi \right) ^{\frac{1}{\xi }} \Big . \\&\times \left( \log (1+\zeta _{i2}\xi ) + \xi \left( \left( \cosh (2{\zeta _{i1}}) - 1 \right) \xi \alpha ^{-2} (2\log (1+\zeta _{i2}\xi ) - \xi - 3) + {\zeta _{i2}}(2\log (1+\zeta _{i2}\xi ) - 1) \right) \right) \\&+2\xi \left( \xi \left( \cosh (2{\zeta _{i1}})- 1\right) \left( -\log (1+\zeta _{i2}\xi )\left( \log (1+\zeta _{i2}\xi ) - 2\xi - 2 \right) - 3\xi - 1 \right) \right. \\&\Big .\left. + {\zeta _{i2}}\alpha ^{2}\left( \log (1+\zeta _{i2}\xi )\left( -\log (1+\zeta _{i2}\xi ) + 2\xi + 1 \right) -\xi \right) \right) \Big ). \end{aligned}$$

For the case \(\xi = 0\), \(\varvec{V}_0 = {{\text{ diag }}(v_{0,1},\ldots ,v_{0,n})}\) and \(\varvec{k}_0 = {(k_{0,1},\ldots ,k_{0,n})}^\top \) defined in (8) have elements

$$\begin{aligned} v_{0,i}= & {} \displaystyle \frac{1}{4\alpha ^2}\left( \alpha (\alpha \text {sech}^2(\zeta _{i1})-\alpha \zeta _{i2}) -2\exp (-\zeta _{i2})\left( 1+\cosh (2\zeta _{i1})-\frac{\alpha ^2\zeta _{i2}}{2}\right) \right) ,\\ k_{0,i}= & {} \displaystyle \frac{\cosh (\zeta _{i1})}{\alpha ^2}(-1+\exp (-\zeta _{i2})(1-\zeta _{i2})). \end{aligned}$$

In addition, we have

$$\begin{aligned} {\displaystyle \ddot{\ell }}_{\alpha \alpha ,0}= & {} \frac{n}{\alpha ^2}-{\sum _{i=1}^{n}}\frac{2\zeta _{i2}}{\alpha ^2}(1-\exp (-\zeta _{i2})) -{\sum _{i=1}^{n}}\frac{\zeta _{i2}^2}{\alpha ^2}\exp (-\zeta _{i2}). \end{aligned}$$

Appendix 2: Data set

Daily maximum ozone concentration data (\(T\), in ppb): 14.290, 40.820, 40.310, 45.920, 8.163, 13.270, 11.220, 21.940, 14.800, 20.920, 38.270, 32.650, 41.330, 46.430, 42.860, 23.980, 27.040, 34.180, 20.410, 11.730, 36.220, 11.220, 31.120, 13.270, 13.270, 16.330, 19.900, 14.800, 15.820, 14.800, 14.800, 18.370, 8.673.

Daily maximum temperature data (\(X\), in \(^{\circ }\)C): 16.8, 24.2, 22.9, 22.5, 15.8, 17.2, 14.8, 18.5, 16.4, 19.8, 26.5, 18.1, 19.1, 22.2, 21.4, 21.3, 25.5, 20.3, 15.7, 16.2, 22.7, 16.8, 22.5, 17.0, 16.5, 17.6, 16.8, 17.9, 18.4, 18.7, 19.9, 17.1, 15.4.

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Leiva, V., Ferreira, M., Gomes, M.I. et al. Extreme value Birnbaum–Saunders regression models applied to environmental data. Stoch Environ Res Risk Assess 30, 1045–1058 (2016). https://doi.org/10.1007/s00477-015-1069-6

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