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Bayesian total loss estimation using shared random effects |
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| Institution: | 1. Laboratory of Exercise Sciences, Department of Physiology and Pharmacology, Fluminense Federal University, Niterói, Brazil;2. Department of Anaesthesiology, The Copenhagen Muscle Research Center, Rigshospitalet, University of Copenhagen, Denmark;3. Faculty of Physical Education, University of Brasília, Brasília, Brazil;1. Department of Cardiac Surgery, IRCCS Policlinico San Donato, Milano, Italy;2. Department of Cardiothoracic and Vascular Anesthesia & ICU, I.R.C.C.S. Policlinico San Donato, Milan, Italy;3. Department of Cardiology, Montefiore Einstein Center for Heart and Vascular Care, Bronx, New York, United States;4. Department of Clinical Sciences and Community Health, University of Milan, Italy;5. Scientific Directorate, IRCCS Policlinico San Donato, Milano, Italy;6. Department of Cardiac Rehabilitation, IRCCS Policlinico San Donato, Milano, Italy;7. Service of Laboratory Medicine, IRCCS Policlinico San Donato, Milan, Italy |
| Abstract: | The pricing of insurance policies requires estimates of the total loss. The traditional compound model imposes an independence assumption on the number of claims and their individual sizes. Bivariate models, which model both variables jointly, eliminate this assumption. A regression approach allows policy holder characteristics and product features to be included in the model. This article presents a bivariate model that uses joint random effects across both response variables to induce dependence effects. Bayesian posterior estimation is done using Markov Chain Monte Carlo (MCMC) methods. A real data example demonstrates that our proposed model exhibits better fitting and forecasting capabilities than existing models. |
| Keywords: | Total loss Claim size Claim count Shared parameter model Dependence Generalized linear mixed model Bayesian inference Markov Chain Monte Carlo |
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