Bayesian criteria for non-zero effects detection under skew-normal search model

Abstract

Shirakura et al. [12] introduced search probability (SP) in order to compare search designs (SD). Afterwards, the SP-based and other related criteria were developed, all for the normal model. In the present study, we considered a general underlying skew-normal (SN) model and obtained new criteria in a simple explicit form using the Bayesian approach. These criteria are design-dependent and hence are able to rank SDs with respect to their search performance. © 2020, National Statistical Institute. All rights reserved.