Bayesian Criteria for Non-Zero Effects Detection Under Skew-Normal Search Model

Sara Sadeghi; Hooshang Talebi

doi:10.57805/revstat.v18i3.303

Bayesian Criteria for Non-Zero Effects Detection Under Skew-Normal Search Model

Authors

Sara Sadeghi University of Isfahan
Hooshang Talebi University of Isfahan

DOI:

https://doi.org/10.57805/revstat.v18i3.303

Keywords:

Bayesian approach, Kullback–Leibler distance, search design, search linear model, skew-normal distribution

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[1]dependent and hence are able to rank SDs with respect to their search performance.

Downloads

REVSTAT2020v18n3_5

Published

2020-08-04

How to Cite

Sadeghi , S., & Talebi , H. (2020). Bayesian Criteria for Non-Zero Effects Detection Under Skew-Normal Search Model. REVSTAT-Statistical Journal, 18(3), 311–323. https://doi.org/10.57805/revstat.v18i3.303