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Commentary on Gronau and Wagenmakers

机译：评论Gronau Wagenmakers

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The three examples Gronau and Wagenmakers (Computational Brain and Behavior, 2018;hereafter denoted G&W) use to demonstrate the limitations of Bayesian forms of leave-one-out cross validation (let us term this LOOCV) for model selection have several important properties: The true model instance is among the model classes being compared;the smaller, simpler model is a point hypothesis that in fact generates the data;the larger class contains the smaller. As G&Wadmit, there is a good deal of prior history pointing to the limitations of cross validation and LOOCV when used in such situations (e.g., Bernardo and Smith 1994). We do not wish to rehash this literature trail, but rather give a conceptual overview of methodology that allows discussion of the ways that various methods of model selection align with scientific practice and scientific inference, and give our recommendation for the simplest approach that matches statistical inference to the needs of science. The methods include minimum description length (MDL) as reported by Grünwald (2007);Bayesian model selection (BMS) as reported by Kass and Raftery (Journal of the American Statistical Association, 90, 773-795, 1995);and LOOCVas reported by Browne (Journal of Mathematical Psychology, 44, 108-132, 2000) and Gelman et al. (Statistics and Computing, 24, 997-1016, 2014). In this commentary, we shall restrict the focus to forms of BMS and LOOCV. In addition, in these days of "Big Data," one wants inference procedures that will give reasonable answers as the amount of data grows large, one focus of the article by G&W. We discuss how the various inference procedures fare when the data grow large.
机译：三个例子Gronau Wagenmakers(计算大脑和行为,2018;以后熟悉公司)表示用于演示的局限性贝叶斯形式的分析验证(让我们学期这个LOOCV)模型选择有几个重要的性质:真正的模型实例模型类相比,是一个规模较小、较为简单的模型点假设实际上生成数据;较大的类包含较小。之前G&Wadmit,有大量的历史指着交叉验证的局限性和LOOCV使用时,在这种情况下(例如,Bernardo史密斯,1994)。重复这个文学,而是给予概念上的方法,允许的概述讨论各种方法的方式模型选择与科学实践一致和科学推理,给我们推荐的最简单的方法比赛统计推断的需要科学。长度(MDL)据。格伦沃尔德(2007年);贝叶斯模型选择(BMS)报道卡斯和阿布(《美国统计协会,90年,773 - 795年,1995年);布朗(《LOOCVas报道数学心理学,44岁,108 - 132,2000)> et al。(统计和计算,24岁,997 - 1016, 2014)。限制对形式的BMS和LOOCV的焦点。此外,在这些天的“大数据”,一个想要的会给合理的推理过程答案随着的数据量越来越大,一个本文在熟悉公司的焦点。各种费用当数据推理过程大的增长。

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