Model Selection Techniques: An Overview

Published

Journal Article

© 2018 IEEE. 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.

Full Text

Duke Authors

Cited Authors

  • Ding, J; Tarokh, V; Yang, Y

Published Date

  • November 1, 2018

Published In

Volume / Issue

  • 35 / 6

Start / End Page

  • 16 - 34

Electronic International Standard Serial Number (EISSN)

  • 1558-0792

International Standard Serial Number (ISSN)

  • 1053-5888

Digital Object Identifier (DOI)

  • 10.1109/MSP.2018.2867638

Citation Source

  • Scopus