Limitations of “Limitations of Bayesian Leave-one-out Cross-Validation for Model Selection”

Aki Vehtari, Daniel P. Simpson, Yuling Yao, Andrew Gelman

Research output: Contribution to journalArticleOtherpeer-review

26 Citations (Scopus)

Abstract

In an earlier article in this journal, Gronau and Wagenmakers (2018) discuss some problems with leave-one-out cross-validation (LOO) for Bayesian model selection. However, the variant of LOO that Gronau and Wagenmakers discuss is at odds with a long literature on how to use LOO well. In this discussion, we discuss the use of LOO in practical data analysis, from the perspective that we need to abandon the idea that there is a device that will produce a single-number decision rule.

Original languageEnglish
Pages (from-to)22-27
Number of pages6
JournalComputational Brain & Behavior
Volume2
Issue number1
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • M-closed
  • M-open
  • Principle of complexity
  • Reality
  • Statistical convenience

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