In Bayesian decision theory, the performance of an action is measured by its pos- terior expected loss. In some cases it may be convenient/necessary to use a non- optimal decision instead of the optimal one. In these cases it is important to quantify the additional loss we incur and evaluate whether to use the non-optimal decision or not. In this article we study the predictive probability distribution of a relative measure of the additional loss and its use to define sample size determination criteria in a general testing set-up.
On the predictive performance of a non-optimal action in hypothesis testing / DE SANTIS, Fulvio; Gubbiotti, Stefania. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1613-981X. - 30:2(2021), pp. 689-709. [10.1007/s10260-020-00539-1]
On the predictive performance of a non-optimal action in hypothesis testing
Fulvio De Santis;Stefania Gubbiotti
2021
Abstract
In Bayesian decision theory, the performance of an action is measured by its pos- terior expected loss. In some cases it may be convenient/necessary to use a non- optimal decision instead of the optimal one. In these cases it is important to quantify the additional loss we incur and evaluate whether to use the non-optimal decision or not. In this article we study the predictive probability distribution of a relative measure of the additional loss and its use to define sample size determination criteria in a general testing set-up.File | Dimensione | Formato | |
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