Model averaging approaches to data subset selection

被引：2

作者：

Neil E.T. ^{[1
]}

Sitison J.W. ^{[1
]}

机构：

[1] Department of Physics, University of Colorado, Boulder, 80309, CO

来源：

Physical Review E | 2023年 / 108卷 / 04期

关键词：

Compendex;

D O I：

10.1103/PhysRevE.108.045308

中图分类号：

学科分类号：

摘要：

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. Analytical and numerical examples are provided. © 2023 American Physical Society.

引用

共 12 条

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