손해보험 위험도 추정에 대한 베이즈 위험 비교 연구

초록

Well-known Bayes and empirical Bayes estimators have a disadvantage in respecting to overshink the parameter estimatorerror; therefore, a constrained Bayes estimator is suggested by matching the first two moments. Also traditional loss function such as mean square error loss function only considers the precision of estimationand to consider both precision and goodness of fit, balanced loss function is suggested. With these reasons, constrained Bayes estimators under balanced loss function is recommended for non-lifeinsurance pricing.; however, most studies focus on the performance of estimation since Bayes risk of newlysuggested estimators such as constrained Bayes and constrained empirical Bayes estimators under specificloss function is difficult to derive. This study compares the Bayes risk of several Bayes estimators under twodifferent loss functions for estimating the risk in the auto insurance business and indicates the effectivenessof the newly suggested Bayes estimators with regards to Bayes risk perspective through auto insurance real dataanalysis.

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