鸽子洞靴带
The pigeonhole bootstrap
摘要：
最近有很多兴趣的数据，在统计语言中，可以被描述为具有一个大的交叉和严重不平衡的随机效应结构。这样的数据集出现推荐引擎和信息检索问题。许多大的二部加权图也具有这种结构。我们想评估适合这些数据的算法的稳定性。即使对于线性统计，一种简单的自举抽样形式也可能严重误导，McCullagh [Bernoulli 6（2000）285- 301]已经表明，没有自举方法是精确的。我们表明，一个替代的自助分别rescue行和列的数据矩阵满足平均值的一致性属性，即使在异方差交叉不平衡随机效应模型。这种替代方法不需要用户将交叉随机效应模型拟合到数据。
英文摘要：
Recently there has been much interest in data that, in statistical language, may be described as having a large crossed and severely unbalanced random effects structure. Such data sets arise for recommender engines and information retrieval problems. Many large bipartite weighted graphs have this structure too. We would like to assess the stability of algorithms fit to such data. Even for linear statistics, a naive form of bootstrap sampling can be seriously misleading and McCullagh [Bernoulli 6 (2000) 285--301] has shown that no bootstrap method is exact. We show that an alternative bootstrap separately resampling rows and columns of the data matrix satisfies a mean consistency property even in heteroscedastic crossed unbalanced random effects models. This alternative does not require the user to fit a crossed random effects model to the data.
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。
