Visualization and assessment of model selection uncertainty

Yichen Qin, Linna Wang, Yang Li and Rong Li

Computational Statistics & Data Analysis, 2023, vol. 178, issue C

Abstract: Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. To this goal, initially several graphical tools are proposed. These include the G-plots and H-plots, to visualize the distribution of the selected model. Then the concept of model selection deviation to quantify the model selection uncertainty is introduced. Similar to the standard error of an estimator, model selection deviation measures the stability of the selected model given by a model selection procedure. For such a measure, a bootstrap estimation procedure is discussed and its desirable performance is demonstrated through simulation studies and real data analysis.

Keywords: Bootstrap; Model selection deviation; Distribution of the selected model (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:178:y:2023:i:c:s0167947322001785

DOI: 10.1016/j.csda.2022.107598

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