@article{0176513da0db4583895a64e59a545003,
title = "Limitations of “Limitations of Bayesian Leave-one-out Cross-Validation for Model Selection”",
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.",
keywords = "M-closed, M-open, Principle of complexity, Reality, Statistical convenience",
author = "Aki Vehtari and Simpson, {Daniel P.} and Yuling Yao and Andrew Gelman",
note = "Funding Information: Open access funding provided by Aalto University. Funding Information: Discussion of Gronau and Wagenmakers () for Computational Brain & Behavior. We thank Paul B{\"u}rkner for helpful comments, and the Academy of Finland, the Canadian Natural Sciences and Engineering Research Council, the U.S. Office of Naval Research, National Science Foundation, and Defense Advanced Research Projects Administration for partial support of this work. Publisher Copyright: {\textcopyright} 2019, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2019",
doi = "10.1007/s42113-018-0020-6",
language = "English",
volume = "2",
pages = "22--27",
journal = "Computational Brain & Behavior",
issn = "2522-0861",
publisher = "Springer",
number = "1",
}