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Model Selection Techniques: An Overview

Ding, Jie and Tarokh, Vahid and Yang, Yuhong (2018) Model Selection Techniques: An Overview. IEEE Signal Processing Magazine, 35 (6). pp. 16-34. ISSN 1053-5888. https://resolver.caltech.edu/CaltechAUTHORS:20181128-150927005

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Abstract

In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus it is central to scientific studies in such fields as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods has been proposed, following different philosophies and exhibiting varying performances. The purpose of this article is to provide a comprehensive overview of them, in terms of their motivation, large sample performance, and applicability. We provide integrated and practically relevant discussions on theoretical properties of state-of-the-art model selection approaches. We also share our thoughts on some controversial views on the practice of model selection.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/MSP.2018.2867638DOIArticle
ORCID:
AuthorORCID
Ding, Jie0000-0002-3584-6140
Tarokh, Vahid0000-0003-2994-6302
Additional Information:© 2018 IEEE. Date of publication: 13 November 2018. This research was funded in part by the Defense Advanced Research Projects Agency under grant W911NF-18-1-0134. We thank Dr. Shuguang Cui and eight anonymous reviewers for giving feedback on the initial submission of the manuscript. We are also grateful to Dr. Matthew McKay and Dr. Osvaldo Simeone for handling the full submission of the manuscript, and to three anonymous reviewers for their comprehensive comments that have greatly improved the article.
Funders:
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)W911NF-18-1-0134
Issue or Number:6
Record Number:CaltechAUTHORS:20181128-150927005
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20181128-150927005
Official Citation:J. Ding, V. Tarokh and Y. Yang, "Model Selection Techniques: An Overview," in IEEE Signal Processing Magazine, vol. 35, no. 6, pp. 16-34, Nov. 2018. doi: 10.1109/MSP.2018.2867638
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91316
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Nov 2018 23:17
Last Modified:03 Oct 2019 20:33

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