Model averaging approaches to data subset selection

Neil, Ethan T.; Sitison, Jacob W.

doi:10.1103/physreve.108.045308

Model averaging approaches to data subset selection

Journal Article · Mon Oct 23 04:00:00 EDT 2023 · Physical Review. E

DOI:https://doi.org/10.1103/physreve.108.045308· OSTI ID:2223083

^[1]; ^[2]

University of Colorado, Boulder, CO (United States); University of Colorado
University of Colorado, Boulder, CO (United States)

Model averaging is a useful and robust method for dealing with model uncertainty in statistical analysis. Often, it is useful to consider data subset selection at the same time, in which model selection criteria are used to compare models across different subsets of the data. Two different criteria have been proposed in the literature for how the data subsets should be weighted. We compare the two criteria closely in a unified treatment based on the Kullback-Leibler divergence and conclude that one of them is subtly flawed and will tend to yield larger uncertainties due to loss of information. Here, analytical and numerical examples are provided.

Research Organization:: University of Colorado, Boulder, CO (United States)

Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: SC0010005

OSTI ID:: 2223083

Journal Information:: Physical Review. E, Journal Name: Physical Review. E Journal Issue: 4 Vol. 108; ISSN 2470-0045

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

References (8)

Information Criteria and Statistical Modeling Konishi, Sadanori; Kitagawa, Genshiro Springer Series in Statistics https://doi.org/10.1007/978-0-387-71887-3	book	October 2007
Constrained curve fitting Lepage, G. P.; Clark, B.; Davies, C. T. H. Nuclear Physics B - Proceedings Supplements, Vol. 106-107 https://doi.org/10.1016/S0920-5632(01)01638-3	journal	March 2002
A high-bias, low-variance introduction to Machine Learning for physicists Mehta, Pankaj; Bukov, Marin; Wang, Ching-Hao Physics Reports, Vol. 810 https://doi.org/10.1016/j.physrep.2019.03.001	journal	May 2019
Leading hadronic contribution to the muon magnetic moment from lattice QCD Borsanyi, Sz.; Fodor, Z.; Guenther, J. N. Nature, Vol. 593, Issue 7857 https://doi.org/10.1038/s41586-021-03418-1	journal	April 2021
Bayesian model averaging for analysis of lattice field theory results Jay, William I.; Neil, Ethan T. Physical Review D, Vol. 103, Issue 11 https://doi.org/10.1103/PhysRevD.103.114502	journal	June 2021
B ( s ) 0 -mixing matrix elements from lattice QCD for the Standard Model and beyond Bazavov, A.; Bernard, C.; Bouchard, C. M. Physical Review D, Vol. 93, Issue 11 https://doi.org/10.1103/PhysRevD.93.113016	journal	June 2016
Ab initio calculation of the neutron-proton mass difference Borsanyi, S.; Durr, S.; Fodor, Z. Science, Vol. 347, Issue 6229 https://doi.org/10.1126/science.1257050	journal	March 2015
On Information and Sufficiency Kullback, S.; Leibler, R. A. The Annals of Mathematical Statistics, Vol. 22, Issue 1 https://doi.org/10.1214/aoms/1177729694	journal	March 1951

Similar Records

Improved information criteria for Bayesian model averaging in lattice field theory

Journal Article · Sun Jan 28 23:00:00 EST 2024 · Physical Review. D. · OSTI ID:2283673

Optimising Foil Selection for Neutron Activation Systems

Journal Article · Tue Jun 14 00:00:00 EDT 2022 · Journal of Fusion Energy · OSTI ID:1872258

Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
Lattice gauge theory
Mathematical physics methods
Probability theory
Stochastic processes & statistics

Model averaging approaches to data subset selection

Citation Formats

References (8)

Similar Records

Related Subjects