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Keywords:
credibility; actuarial science; outliers; missing observations; robust Kalman filter; shrinkage; time series; risk
credibility; actuarial science; outliers; missing observations; robust Kalman filter; shrinkage; time series; risk
Summary:
In actuarial practice the credibility models must face the problem of outliers and missing observations. If using the $M$-estimation principle from robust statistics in combination with Kalman filtering one obtains the solution of this problem that is acceptable in the numerical framework of the practical actuarial credibility. The credibility models are classified as static and dynamic in this paper and the shrinkage is used for the final ratemaking.
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