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Monte Carlo evidence on the estimation of AR(1) panel data sample selection models

Jiménez-Martín, Sergi; Labeaga, José M. - 2016

Persistent link: https://www.econbiz.de/10011649336

Profit-oriented partial disassembly line design : dealing with hazardous parts and task processing times uncertainty

Bentaha, Mohand Lounes; Dolgui, Alexandre; Battaïa, Olga; … - In: International journal of production research 56 (2018) 24, pp. 7220-7242

Persistent link: https://www.econbiz.de/10011986934

Rare-event simulation for distribution networks

Blanchet, Jose; Li, Juan; Nakayama, Marvin K. - In: Operations research 67 (2019) 5, pp. 1383-1396

Persistent link: https://www.econbiz.de/10012107789

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

Parameter estimation in multivariate logit models with many binary choices

Bel, Koen; Fok, Dennis; Paap, Richard - In: Econometric reviews 37 (2018) 1/5, pp. 534-550

Persistent link: https://www.econbiz.de/10012039382

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

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

The Gibbs sampler with particle efficient importance sampling for state-space models

Grothe, Oliver; Kleppe, Tore Selland; Liesenfeld, Roman - In: Econometric reviews 38 (2019) 10, pp. 1152-1175

Persistent link: https://www.econbiz.de/10012181399

The stochastic collocation Monte Carlo sampler : highly efficient sampling from "expensive" distributions

Grzelak, Lech A.; Witteveen, J. A. S.; Suárez-Taboada, M. - In: Quantitative finance 19 (2019) 2, pp. 339-356

Persistent link: https://www.econbiz.de/10012194657

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