On the predictive performance of a non-optimal action in hypothesis testing

Fulvio De Santis and Stefania Gubbiotti ()
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Fulvio De Santis: Sapienza Università di Roma
Stefania Gubbiotti: Sapienza Università di Roma

Statistical Methods & Applications, 2021, vol. 30, issue 2, No 12, 689-709

Abstract: Abstract In Bayesian decision theory, the performance of an action is measured by its posterior 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.

Keywords: Bayesian inference; Experimental design; Predictive analysis; Sample size determination; Statistical decision theory (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10260-020-00539-1

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