Robust Bayesian Experience Rating

Different rating methods which allow for exceptional large claims are discussed. A robust Bayesian statistical model is proposed which can cope with non negative, skewed data. An example from fire insurance is analyzed. The performance of the posterior mean is compared to the performance of a robust credibility estimator.

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Robust Bayesian Experience Rating

Astin Bulletin ◽

10.1017/s0515036100013921 ◽

2004 ◽

Vol 34 (1) ◽

pp. 125-150 ◽

Cited By ~ 1

Author(s):

René Schnieper

Keyword(s):

Statistical Model ◽

Skewed Data ◽

Experience Rating ◽

Posterior Mean ◽

Credibility Estimator ◽

Fire Insurance

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Imprecise credibility theory

Annals of Actuarial Science ◽

10.1017/s1748499521000117 ◽

2021 ◽

pp. 1-15

Author(s):

Liang Hong ◽

Ryan Martin

Keyword(s):

Real World ◽

Prior Information ◽

Model Specification ◽

Credibility Theory ◽

Experience Rating ◽

Credibility Estimator ◽

Real World Applications ◽

Property And Casualty Insurance ◽

Doubly Robust ◽

Interval Estimator

Abstract The classical credibility theory is a cornerstone of experience rating, especially in the field of property and casualty insurance. An obstacle to putting the credibility theory into practice is the conversion of available prior information into a precise choice of crucial hyperparameters. In most real-world applications, the information necessary to justify a precise choice is lacking, so we propose an imprecise credibility estimator that honestly acknowledges the imprecision in the hyperparameter specification. This results in an interval estimator that is doubly robust in the sense that it retains the credibility estimator’s freedom from model specification and fast asymptotic concentration, while simultaneously being insensitive to prior hyperparameter specification.

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The Linear Markov Property in Credibility Theory

Astin Bulletin ◽

10.2143/ast.17.1.2014984 ◽

1987 ◽

Vol 17 (1) ◽

pp. 71-84 ◽

Cited By ~ 8

Author(s):

Thomas Witting

Keyword(s):

Sufficient Conditions ◽

Markov Property ◽

Recent Time ◽

Necessary And Sufficient Conditions ◽

Credibility Theory ◽

Experience Rating ◽

Credibility Estimator ◽

Time Period ◽

Loss Reserving ◽

Necessary And Sufficient

AbstractWe study the linear Markov property, i.e. the possibility of basing the credibility estimator on data of the most recent time period without loss of accuracy. Necessary and sufficient conditions are derived generally. The meaning of the linear Markov property is also discussed in different experience rating and loss reserving models.

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