数据子集选择的模型平均方法
Model averaging approaches to data subset selection
AI总结：
本文基于 Kullback-Leibler 散度统一比较了数据子集选择的两种加权准则，发现其中一种因信息丢失存在细微缺陷并导致更大不确定性。
摘要：
模型平均是处理统计分析中模型不确定性的一种有用且稳健的方法。通常，同时考虑数据子集选择是有用的，其中模型选择准则用于比较不同数据子集上的模型。文献中提出了两种不同的准则来决定如何对数据子集进行加权。我们在基于 Kullback-Leibler 散度的统一处理下仔细比较了这两种准则，并得出结论：其中一种准则存在细微缺陷，并且由于信息丢失往往会带来更大的不确定性。文中提供了分析和数值示例。
英文摘要：
Model averaging is a useful and robust method for dealing with model uncertainty in statistical analysis. Often, it is useful to consider data subset selection at the same time, in which model selection criteria are used to compare models across different subsets of the data. Two different criteria have been proposed in the literature for how the data subsets should be weighted. We compare the two criteria closely in a unified treatment based on the Kullback-Leibler divergence, and conclude that one of them is subtly flawed and will tend to yield larger uncertainties due to loss of information. Analytical and numerical examples are provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
