References
- Abu Bakar S. A., Hamzah N. A., Maghsoudi M. & Nadarajah S. (2015). Modeling loss data using composite models. Insurance: Mathematics and Economics 61, 146–154.
- Albrecher H., Bierlant J. & Teugels J. (2017). Reinsurance: Actuarial and Statistical Aspects. Chichester: John Wiley & Sons Inc.
- Behrens C. N., Lopes H. F. & Gamerman D. (2004). Bayesian analysis of extreme events with threshold estimation. Statistical Modelling 4(3), 227–244.
- Beirlant G., Segers J. L. & Teugels D. (2004). Statistics of extremes. Chichester: John Wiley & Sons.
- Bernton E., Jacob P. E., Gerber M. & Robert C. P. (2019). Approximate bayesian computation with the wasserstein distance. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81(2), 235–269.
- Bernton E., Jacob P. E., Gerber M. & Robert C. P. (2019). On parameter estimation with the wasserstein distance. Information and Inference: A Journal of the IMA 8(4), 657–676.
- Bladt M., Albrecher H. & Beirlant J. (2020). Threshold selection and trimming in extremes. Extremes 23(4), 629–665.
- Cabras S. & Castellanos M. E. (2011). A bayesian approach for estimating extreme quantiles under a semiparametric mixture model. ASTIN Bulletin 41(1), 87–106.
- Caeiro F. & Gomes M. I. (2015). Threshold selection in extreme value analysis. In Extreme value modeling and risk analysis: methods and applications. Boca Raton, FL: Chapman-Hall/CRC. P. 69–82.
- Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B., Betancourt M., Brubaker M., Guo J., Li P. & Riddell A. (2017). Stan: a probabilistic programming language. Journal of Statistical Software 76(1), 1–32.
- Cooray K. & Ananda M. (2005). Modeling actuarial data with a composite lognormal-pareto model. Scandinavian Actuarial Journal 2005(5), 321–334.
- Danielsson J., Ergun L. M., de Haan L. & de Vries C. G. (2016). Tail index estimation: quantile driven threshold selection. Available at SSRN 2717478.
- Davison A. C. (2011). Statistical models. Cambridge University Press.
- Del Moral P., Doucet A. & Jasra A. (2006). Sequential monte carlo samplers. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68(3), 411–436.
- Diaconis P. & Ylvisaker D. S. (1979). Conjugate priors for exponential families. The Annals of Statistics 7(2), 269–281.
- Drees H. & Kaufmann E. (1998). Selecting the optimal sample fraction in univariate extreme value estimation. Stochastic Processes and Their Applications 75(2), 149–172.
- Gelman A. J. B., Carlin H. S., Dunson S. D. B. & Vehtari A. (2013). Bayesian data analysis. Taylor & Francis Ltd.
- Gelman A., Hwang J. & Vehtari A. (2013). Understanding predictive information criteria for bayesian models. Statistics and Computing 24(6), 997–1016.
- Gerber M., Chopin N. & Whiteley N. (2019). Negative association, ordering and convergence of resampling methods. The Annals of Statistics 47(4), 2236–2260.
- Gerstengarbe F. W. & Werner P. C. (1989). A method for the statistical definition of extreme-value regions and their application to meteorological time series. Zeitschrift fuer Meteorologie; (German Democratic Republic).
- Ghosh S. & Resnick S. (2010). A discussion on mean excess plots. Stochastic Processes and Their Applications 120(8), 1492–1517.
- Goffard P.-O. & Laub P. J. (2021). Approximate bayesian computations to fit and compare insurance loss models. Insurance: Mathematics and Economics 100, 350–371.
- Gomes M. I., Figueiredo F. & Neves M. M. (2011). Adaptive estimation of heavy right tails: resampling-based methods in action. Extremes 15(4), 463–489.
- Grün B. & Miljkovic T. (2019). Extending composite loss models using a general framework of advanced computational tools. Scandinavian Actuarial Journal 2019(8), 642–660.
- Guillou A. & Hall P. (2001). A diagnostic for selecting the threshold in extreme value analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63(2), 293–305.
- Hall P. (1990). Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems. Journal of Multivariate Analysis 32(2), 177–203.
- Hall P. & Welsh A. H. (1985). Adaptive estimates of parameters of regular variation. The Annals of Statistics 13(1), 331–341.
- Hill B. M. (1975). A simple general approach to inference about the tail of a distribution. The Annals of Statistics 3(5), 1163–1174.
- Hoeting J. A., Madigan D., Raftery A. E. & Volinsky C. T. (1999). Bayesian model averaging: a tutorial. Statistical Science 14(4), 382–401.
- Jasra A., Stephens D. A., Doucet A. & Tsagaris T. (2010). Inference for Lévy-driven stochastic volatility models via adaptive sequential monte carlo. Scandinavian Journal of Statistics 38(1), 1–22.
- Kass R. E. & Raftery A. E. (1995). Bayes factors. Journal of the American Statistical Association 90(430), 773–795.
- Klugman S. A., Panjer H. H. & Willmot G. E. (2019). Loss models: from data to decisions. Wiley.
- Kong A., Liu J. S. & Wong W. H. (1994). Sequential imputations and bayesian missing data problems. Journal of the American Statistical Association 89(425), 278–288.
- Lunn D. J., Thomas A., Best N. & Spiegelhalter D. (2000). Winbugs-a bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10(4), 325–337.
- MacDonald A., Scarrott C. J., Lee D., Darlow B., Reale M. & Russell G. (2011). A flexible extreme value mixture model. Computational Statistics & Data Analysis 55(6), 2137–2157.
- Neal R. M. (2001). Annealed importance sampling. Statistics and Computing 11(2), 125–139.
- Nguyen T. L. T., Septier F., Peters G. W. & Delignon Y. (2016). Efficient sequential monte-carlo samplers for bayesian inference. IEEE Transactions on Signal Processing 64(5), 1305–1319.
- Plummer M. (2003). Jags: a program for analysis of bayesian graphical models using gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vol. 124. Vienna, Austria. P. 1–10.
- Reynkens T., Verbelen R., Beirlant J. & Antonio K. (2017). Modelling censored losses using splicing: a global fit strategy with mixed erlang and extreme value distributions. Insurance: Mathematics and Economics 77, 65–77.
- Roberts G. O., Gelman A. & Gilks W. R. (1997). Weak convergence and optimal scaling of random walk metropolis algorithms. The Annals of Applied Probability 7(1), 110–120.
- Salvatier J., Wiecki T. V. & Fonnesbeck C. (2016). Probabilistic programming in python using PyMC3. PeerJ Computer Science 2, e55.
- Scarrott C. & MacDonald A. (2012). A review of extreme value threshold es-timation and uncertainty quantification. REVSTAT–Statistical Journal 10(1), 33–60.
- Scollnik D. P. M. (2007). On composite lognormal-pareto models. Scandinavian Actuarial Journal 2007(1), 20–33.
- Scollnik D. P. M. & Sun C. (2012). Modeling with weibull-pareto models. North American Actuarial Journal 16(2), 260–272.
- South L. F., Pettitt A. N. & Drovandi C. C. (2019). Sequential Monte Carlo samplers with independent markov chain Monte Carlo proposals. Bayesian Analysis 14(3), 753–776.
- Spiegelhalter D. J., Best N. G., Carlin B. P. & van der Linde A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583–639.
- Tancredi A., Anderson C. & O'Hagan A. (2006). Accounting for threshold uncertainty in extreme value estimation. Extremes 9(2), 87–106.
- Vaz de Melo Mendes B. & Lopes H. F. (2004). Data driven estimates for mixtures. Computational Statistics & Data Analysis 47(3), 583–598.
- Wang Y., Hobæk Haff I. & Huseby A. (2020). Modelling extreme claims via composite models and threshold selection methods. Insurance: Mathematics and Economics 91, 257–268.
- Watanabe S. (2010). Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 11(116), 3571–3594.
- Yao Y., Vehtari A., Simpson D. & Gelman A. (2018). Using stacking to average Bayesian predictive distributions (with discussion). Bayesian Analysis 13(3), 917–1007.