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Title: A novel approach to the bias-variance problem in bump hunting

Abstract

This study explores various data-driven methods for performing background-model selection, and for assigning uncertainty on the signal-strength estimator that arises due to the choice of background model. The performance of these methods is evaluated in the context of several realistic example problems. Furthermore, a novel strategy is proposed that greatly simplifies the process of performing a bump hunt when little is assumed to be known about the background. This new approach is shown to greatly reduce the potential bias in the signal-strength estimator, without degrading the sensitivity by increasing the variance, and to produce confidence intervals with valid coverage properties.

Authors:
 [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Lab. for Nuclear Science
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1535615
Grant/Contract Number:  
SC0010497
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 12; Journal Issue: 09; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
Instruments & Instrumentation

Citation Formats

Williams, M. A novel approach to the bias-variance problem in bump hunting. United States: N. p., 2017. Web. doi:10.1088/1748-0221/12/09/p09034.
Williams, M. A novel approach to the bias-variance problem in bump hunting. United States. doi:10.1088/1748-0221/12/09/p09034.
Williams, M. Thu . "A novel approach to the bias-variance problem in bump hunting". United States. doi:10.1088/1748-0221/12/09/p09034. https://www.osti.gov/servlets/purl/1535615.
@article{osti_1535615,
title = {A novel approach to the bias-variance problem in bump hunting},
author = {Williams, M.},
abstractNote = {This study explores various data-driven methods for performing background-model selection, and for assigning uncertainty on the signal-strength estimator that arises due to the choice of background model. The performance of these methods is evaluated in the context of several realistic example problems. Furthermore, a novel strategy is proposed that greatly simplifies the process of performing a bump hunt when little is assumed to be known about the background. This new approach is shown to greatly reduce the potential bias in the signal-strength estimator, without degrading the sensitivity by increasing the variance, and to produce confidence intervals with valid coverage properties.},
doi = {10.1088/1748-0221/12/09/p09034},
journal = {Journal of Instrumentation},
number = 09,
volume = 12,
place = {United States},
year = {2017},
month = {9}
}

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Works referenced in this record:

Trial factors for the look elsewhere effect in high energy physics
journal, October 2010


Estimating the Dimension of a Model
journal, March 1978


Model Selection: An Integral Part of Inference
journal, June 1997

  • Buckland, S. T.; Burnham, K. P.; Augustin, N. H.
  • Biometrics, Vol. 53, Issue 2
  • DOI: 10.2307/2533961

The Focused Information Criterion
journal, December 2003

  • Claeskens, Gerda; Hjort, Nils Lid
  • Journal of the American Statistical Association, Vol. 98, Issue 464
  • DOI: 10.1198/016214503000000819

Handling uncertainties in background shapes: the discrete profiling method
journal, April 2015


Model selection for amplitude analysis
journal, September 2015