STI Publications - View Publication Form #15770
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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 |
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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 |
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Issue | |
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Attachments/Datasets/DOI Link
Document(s) |
bias1.pdf
(STI Document)
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Dataset(s) | (none) |
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