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Computational Bayesian Methods for Insurance Premium Estimation

Title:

Computational Bayesian Methods for Insurance Premium Estimation

Quijano Xacur, Oscar Alberto / OAQX (2019) Computational Bayesian Methods for Insurance Premium Estimation. PhD thesis, Concordia University.

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Abstract

Bayesian Inference is used to develop a credibility estimator and a
method to compute insurance premium risk loadings. Algorithms to
apply both methods to Generalized Linear Models (GLMs) are provided.
We call our credibility estimator the entropic premium. It is
a Bayesian point estimator that uses the relative entropy as the loss
function. The risk measures Value-at-Risk (VaR) and
Tail-Value-at-Risk (TVaR) are used to determine premium risk
loadings. Our method considers the number of insureds and their
durations as random variables. A distribution to model the duration
of risks is introduced. We call it unifed, it has support on
the interval (0,1), it is an exponential dispersion family and it
can be used as the response distribution of a GLM.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (PhD)
Authors:Quijano Xacur, Oscar Alberto / OAQX
Institution:Concordia University
Degree Name:Ph. D.
Program:Mathematics
Date:July 2019
Thesis Supervisor(s):Garrido, José
Keywords:Credibility Theory Generalized Linear Models Exponential Dispersion Families MCMC Premium loading
ID Code:985817
Deposited By: OSCAR ALBERTO QUIJANO XACUR
Deposited On:14 Nov 2019 18:39
Last Modified:14 Nov 2019 18:39
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