标题： 对García-Donato等（2025）的“模型不确定性和缺失数据：一种客观贝叶斯观点”的评论 显示英文标题

标题： Comment on García-Donato et al. (2025) "Model uncertainty and missing data: An objective Bayesian perspective"

摘要： Garcia-Donato 等人（2025）提出了一种在模型选择问题中处理缺失数据的方法，该方法使用了客观贝叶斯方法。当前的评论讨论了针对此问题的一种现有客观贝叶斯方法。首先，而不是使用g先验，基于最小分数，采用了O'Hagan的分式贝叶斯因子（O'Hagan，1995）。其次，更重要的是由于关注缺失数据，Rubin的多重插补规则可以直接用于分式贝叶斯因子，因为分式贝叶斯因子可以表示为变量选择问题的Savage-Dickey密度比。当前的评论推导了变量选择问题的方法。此外，其隐含行为在一个数值实验中得到说明，显示了与Garcia-Donato 等人（2025）的方法相当的结果。 显示英文摘要

摘要： Garcia-Donato et al. (2025) present a methodology for handling missing data in a model selection problem using an objective Bayesian approach. The current comment discusses an alternative, existing objective Bayesian method for this problem. First, rather than using the g prior, O'Hagan's fractional Bayes factor (O'Hagan, 1995) is utilized based on a minimal fraction. Second, and more importantly due to the focus on missing data, Rubin's rules for multiple imputation can directly be used as the fractional Bayes factor can be written as a Savage-Dickey density ratio for a variable selection problem. The current comment derives the methodology for a variable selection problem. Moreover, its implied behavior is illustrated in a numerical experiment, showing competitive results as the method of Garcia-Donato et al. (2025).