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September 2018 A frequency-calibrated Bayesian search for new particles
Shirin Golchi, Richard Lockhart
Ann. Appl. Stat. 12(3): 1939-1968 (September 2018). DOI: 10.1214/18-AOAS1138

Abstract

The statistical procedure used in the search for new particles is investigated in this paper. The discovery of the Higgs particles is used to lay out the problem and the existing procedures. A Bayesian hierarchical model is proposed to address inference about the parameters of interest while incorporating uncertainty about the nuisance parameters into the model. In addition to inference, a decision making procedure is proposed. A loss function is introduced that mimics the important features of a discovery problem. Given the importance of controlling the “false discovery” and “missed detection” error rates in discovering new phenomena, the proposed procedure is calibrated to control for these error rates.

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Shirin Golchi. Richard Lockhart. "A frequency-calibrated Bayesian search for new particles." Ann. Appl. Stat. 12 (3) 1939 - 1968, September 2018. https://doi.org/10.1214/18-AOAS1138

Information

Received: 1 May 2016; Revised: 1 January 2018; Published: September 2018
First available in Project Euclid: 11 September 2018

zbMATH: 06979658
MathSciNet: MR3852704
Digital Object Identifier: 10.1214/18-AOAS1138

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 3 • September 2018
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