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|Title: ||Predictive Modeling of Insurance Claims Using Reversible Jump Markov Chain Monte Carlo Methods|
|Authors: ||Duah, Collins Afranie|
|Issue Date: ||19-Jan-2017|
|Abstract: ||There has been considerable amount of attention rendered to claims reserving
methods over the last few decades in actuarial science. The commonly used
method of estimating claims reserves is the chain ladder technique. The underlying
principle of the chain-ladder technique is that no underlying pattern to the
run-o , and that each development year should be allocated a separate parameter.
Applicable to a wide range of data, the chain ladder could alternatively
be condemned for having too many parameters and also assumptions have to be
used to estimate reserves beyond the latest development year already observed.
This research seeks to explain an approach to model the development of claims
run-o , using reversible jump Markov Chain Monte Carlo (RJMCMC) method.
The study uses claims data from a renowed Insurance Company in Ghana; Win-
BUGS the tool used in simulating the reversible jump Markov Chain Monte
Carlo (RJMCMC) method. The Bayesian methods are found to be better than
the Over-dispersed Poisson model with lower predictive errors.|
|Description: ||A Thesis submitted to the Institute of Distance Learning,
Department Of Mathematics, Kwame Nkrumah University
Of Science and Technology in partial fulfillment of the
requirements for the Degree of M.Sc. Actuarial Science, 2016|
|Appears in Collections:||Distance Learning|
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