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Publication Information
Title Bias-Variance Trade-off and Model Selection for Proton Radius Extractions
Abstract Intuitively, a scientist might assume that a more complex regression model will necessarily yield a better predictive model of experimental data. Herein, we disprove this notion in the context of extracting the proton charge radius from charge form factor data. Using a Monte Carlo study, we show that a simpler regression model can in certain cases be the better predictive model. This is especially true with noisy data where the complex model will fit the noise instead of the physical signal. Thus, in order to select the appropriate regression model to employ, a clear technique should be used such as the Akaike information criterion or Bayesian information criterion, and ideally selected previous to seeing the results. Also, to ensure a reasonable fit, the scientist should also make regression quality plots, such as residual plots, and not just rely on a single criterion such as reduced chi2. When we apply these techniques to low four-momentum transfer cross section data, we find a prot
Author(s) Douglas Higinbotham, Pablo Giuliani, R. McClellan, Simon Sirca , Xuefei Yan
Publication Date To Appear in 2021
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Exp Nuclear Physics / Experimental Halls / Hall A
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-PHY-19-2851 OSTI Number:
LANL Number: 1812.05706 Other Number: DOE/OR/23177-4625
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Physical Review X
Refereed No
Volume
Issue
Page(s)
Attachments/Datasets/DOI Link
Document(s)
bias1.pdf (STI Document)
Dataset(s) (none)
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