Information criteria for model selection

Jiawei Zhang, Yuhong Yang, Jie Ding

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations


The rapid development of modeling techniques has brought many opportunities for data-driven discovery and prediction. However, this also leads to the challenge of selecting the most appropriate model for any particular data task. Information criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), have been developed as a general class of model selection methods with profound connections with foundational thoughts in statistics and information theory. Many perspectives and theoretical justifications have been developed to understand when and how to use information criteria, which often depend on particular data circumstances. This review article will revisit information criteria by summarizing their key concepts, evaluation metrics, fundamental properties, interconnections, recent advancements, and common misconceptions to enrich the understanding of model selection in general. This article is categorized under: Data: Types and Structure > Traditional Statistical Data Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Information Theoretic Methods Statistical Models > Model Selection.

Original languageEnglish (US)
Article numbere1607
JournalWiley Interdisciplinary Reviews: Computational Statistics
Issue number5
StatePublished - Sep 1 2023

Bibliographical note

Funding Information:
J. Zhang was supported in part by the National Science Foundation under grant number ECCS‐2038603. J. Ding was supported in part by the National Science Foundation under grant number DMS‐2134148.

Publisher Copyright:
© 2023 The Authors. WIREs Computational Statistics published by Wiley Periodicals LLC.


  • Akaike information criterion
  • Bayesian information criterion
  • information criteria
  • model selection
  • variable selection


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