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The cascade Bayesian approach : prior transformation for a controlled integration of internal data, external data and scenarios
Hassani, Bertrand; Renaudin, Alexis - In: Risks : open access journal 6 (2018) 2, pp. 1-17
According to the last proposals of the Basel Committee on Banking Supervision, banks or insurance companies under the advanced measurement approach (AMA) must use four different sources of information to assess their operational risk capital requirement. The fourth includes ’business...
Persistent link: https://www.econbiz.de/10011866503
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Modeling operational risk data reported above a time-varying threshold
Shevchenko, Pavel V.; Temnov, Grigory - In: The journal of operational risk 4 (2009/10) 2, pp. 19-42
Persistent link: https://www.econbiz.de/10003883151
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Evolutionary sequential Monte Carlo samplers for change-point models
Dufays, Arnaud - In: Econometrics : open access journal 4 (2016) 1, pp. 1-33
Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC...
Persistent link: https://www.econbiz.de/10011504888
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To bridge, to warp or to wrap? : a comparative study of Monte Carlo methods for efficient evaluation of marginal likelihoods
Ardia, David; Hoogerheide, Lennart; Dijk, Herman K. van - 2009
importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …
Persistent link: https://www.econbiz.de/10011377602
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When random assigment is not enough : accounting
Feinberg, Fred M.; Salisbury, Linda Court; Ying, Yuanping - In: Marketing science : the marketing journal of the … 35 (2016) 6, pp. 976-994
Persistent link: https://www.econbiz.de/10011617492
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Fast, approximate MCMC for Bayesian analysis of large data sets : a design based approach
Kaeding, Matthias - 2016
. The subsample is taken via the cube method, a balanced sampling design, which is defined by the property that the sample … dass dieser nicht gespeichert werden muss. Die Stichprobe wird via cube sampling, einem balanciertem Stichprobendesign …
Persistent link: https://www.econbiz.de/10011566817
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Speeding up MCMC by efficient data subsampling
Quiroz, Matias; Villani, Mattias; Kohn, Robert - 2015
subsets are selected using an efficient Probability Proportional-to-Size (PPS) sampling scheme, where the inclusion … applications. We propose a simple way to adaptively choose the sample size m during the MCMC to optimize sampling efficiency for a …
Persistent link: https://www.econbiz.de/10010500806
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Bayesian modelling, Monte Carlo sampling and capital allocation of insurance risks
Peters, Gareth; Targino, Rodrigo S.; Wüthrich, Mario V. - In: Risks : open access journal 5 (2017) 4, pp. 1-51
The main objective of this work is to develop a detailed step-by-step guide to the development and application of a new class of efficient Monte Carlo methods to solve practically important problems faced by insurers under the new solvency regulations. In particular, a novel Monte Carlo method...
Persistent link: https://www.econbiz.de/10011783091
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Joint Bayesian analysis of oarameters and states in nonlinear non‐Gaussian state space models
Barra, István; Hoogerheide, Lennart; Koopman, Siem Jan; … - In: Journal of applied econometrics 32 (2017) 5, pp. 1003-1026
Persistent link: https://www.econbiz.de/10011862307
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Bayesian mortality forecasting with overdispersion
Wong, Jackie S. T.; Forster, Jonathan J.; Smith, Peter W. F. - In: Insurance / Mathematics & economics 83 (2018), pp. 206-221
Persistent link: https://www.econbiz.de/10011944142