Sequential testing identifies conditions for full credibility in insured cohorts, suggesting improved decision-making processes.
This paper introduces sequential statistical methods in actuarial science. As an example of sequential decision making that is based on the data accrued in real time, it focuses on sequential testing for full credibility. Classical statistical tools are used to determine the stopping time and the terminal decision that controls the overall error rate and power of the procedure. As a result, a set of conditions is obtained under which an insured cohort becomes fully credible.
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Baron et al. (2017) studied this question.
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