Exact Inference for Complex Clustered Data Using Within-Cluster Resampling
Exact Inference for Complex Clustered Data Using Within-Cluster Resampling
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DOI：
10.1080/10543401003618884
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发表时间：
2010-01-01
影响因子：
1.1
通讯作者：
Fay, Michael
中科院分区：
文献类型：
--
作者：
Follmann, Dean;Fay, Michael
本文介绍了精确的置换方法，当有独立的集群的数据与任意的集群内的相关性。为了消除聚类问题，我们从每个聚类中随机选择一个数据点，并为这个独立的数据计算我们的测试统计量和所有可能排列的相关支持点。虽然这显然是有效的，但也是低效的。我们重复此过程，直到创建了所有可能的独立数据集，并使用随机创建的数据集的平均支持点作为平均测试统计量的参考分布。这种方法使用所有的数据，是一个置换扩展的集群内resource（WCR）。我们讨论了精确和蒙特卡洛版本的方法，并将其应用到几个数据集。WCR置换可以应用在相当一般的设置时，集群内的相关性是一个滋扰和精确的推理是必要的。
This paper introduces exact permutation methods for use when there are independent clusters of data with arbitrary within-cluster correlation. To eliminate the problem of clustering, we randomly select a data point from each cluster and for this now independent data, and calculate our test statistic and the associated support points for all possible permutations. While clearly valid, this is also inefficient. We repeat this process until all possible independent data sets have been created and use the support points averaged over the randomly created data sets as our reference distribution for the averaged test statistic. This approach uses all of the data and is a permutation extension of within-cluster resampling (WCR). We discuss both exact and Monte Carlo versions of the approach and apply it to several data sets. WCR permutation can be applied in quite general settings when within cluster correlation is a nuisance and exact inference is necessary.