References
- C.N Behrens, H.F Lopes, and D. Gamerman, Bayesian analysis of extreme events with threshold estimation, Stat. Model. 4 (2004), pp. 227–244. doi: https://doi.org/10.1191/1471082X04st075oa
- S.S. Cabras, M.E. Castellanos, and D. Gamerman, A default Bayesian approach for regression on extremes, Stat. Model. 11 (2011), pp. 557–580. doi: https://doi.org/10.1177/1471082X1001100606
- S.G. Coles, An Introduction to Statistical Modelling of Extreme Values, Springer-Verlag, London, 2001.
- M.K. Cowles and B.P. Carlin, Markov chain Monte Carlo convergence diagnostics: A comparative review, J. Am. Stat. Assoc. 91 (1996), pp. 883–904. doi: https://doi.org/10.1080/01621459.1996.10476956
- C. Cunnane, A note on the Poisson assumption in partial duration series models, Water Resour. Res. 15 (1979), pp. 489–494. doi: https://doi.org/10.1029/WR015i002p00489
- A.C. Davison and R.L. Smith, Models for exceedances over high thresholds (with discussion), J. R. Stat. Soc. Ser. B 52 (1990), pp. 393–342.
- P. Embrechts, C. Küppelberg, and T. Mikosch, Modelling Extremal Events for Insurance and Finance, Springer, New York, 1997.
- D. Gamerman and H.F. Lopes, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd ed., Chapman and Hall/CRC, Baton Rouge, 2006.
- A. Gelman and D.B. Rubin, Inference from iterative simulation using multiple sequences (with discussion), Stat. Sci. 7 (1992), pp. 457–511. doi: https://doi.org/10.1214/ss/1177011136
- J.E. Heffernan and J.A. Tawn, A conditional approach for multivariate extreme values (with discussion), J. R. Stat. Soc. Ser. B 66 (2004), pp. 497–546. doi: https://doi.org/10.1111/j.1467-9868.2004.02050.x
- S.R. Lima, F.F. Nascimento, and V.R.S. Ferraz, Regression models for time-varying extremes, J. Stat. Comput. Simul. 88 (2018), pp. 235–249. doi: https://doi.org/10.1080/00949655.2017.1385788
- B.V.M. Mendes and L.R. Pericchi, Assessing conditional extremal risk of flooding in Puerto Rico, Stoch. Environ. Res. Risk Assess. 23 (2009), pp. 399–410. doi: https://doi.org/10.1007/s00477-008-0220-z
- F.F. do Nascimento, D. Gamerman, and H.F. Lopes, Regression models for exceedance data via the full likelihood, Environ. Ecol. Stat. 18 (2011), pp. 495–512. doi: https://doi.org/10.1007/s10651-010-0148-6
- F.F. do Nascimento, D. Gamerman, and H.F. Lopes, A semiparametric Bayesian approach to extreme value estimation, Stat. Comput. 22 (2012), pp. 661–675. doi: https://doi.org/10.1007/s11222-011-9270-z
- C. Parmesan, T.L. Root, and M.R. Willing, Impacts of extreme weather and climate on terrestrial biota, Bull. Am. Meteorol. Soc. 81 (2000), pp. 443–450. doi: https://doi.org/10.1175/1520-0477(2000)081<0443:IOEWAC>2.3.CO;2
- J. Pickands III, Statistical inference using extreme order statistics, Ann. Stat. 3 (1975), pp. 119–131. doi: https://doi.org/10.1214/aos/1176343003
- H. Sang and A.E. Gelfand, Hierarchical modeling for extreme values observed over space and time, Environ. Ecol. Stat. 16 (2009), pp. 407–426. doi: https://doi.org/10.1007/s10651-007-0078-0
- D.J. Spiegelhalter, N.G. Best, B.P. Carlin, and A. van der Linde, Bayesian measures of model complexity and fit, J. R. Stat. Soc. B 64 (2002), pp. 583–639. doi: https://doi.org/10.1111/1467-9868.00353
- M. Wiper, D.R. Insua, and F. Ruggeri, Mixtures of Gamma distributions with applications, J. Comput. Graph. Stat. 10 (2001), pp. 440–454. doi: https://doi.org/10.1198/106186001317115054