A METHOD FOR GENERATING REALISTIC CORRELATION MATRICES
A METHOD FOR GENERATING REALISTIC CORRELATION MATRICES
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DOI：
10.1214/13-aoas638
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发表时间：
2013-09-01
影响因子：
1.8
通讯作者：
Golan, David
中科院分区：
文献类型：
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作者：
Hardin, Johanna;Garcia, Stephan Ramon;Golan, David
模拟样本相关矩阵在统计学的许多领域都很重要。诸如生成高斯数据并找到它们的样本相关矩阵或生成随机均匀[- 1,1]偏差作为成对相关的方法都有缺点。我们开发了一种算法，以高度可控的方式将噪声添加到一般相关矩阵中。在许多情况下，我们的方法产生的结果优于通过简单地模拟高斯数据获得的结果。此外，我们还演示了如何将我们的通用算法定制为许多不同的相关模型。使用我们的结果与几个不同的应用程序，我们表明模拟相关矩阵可以帮助评估统计方法。
Simulating sample correlation matrices is important in many areas of statistics. Approaches such as generating Gaussian data and finding their sample correlation matrix or generating random uniform [-1, 1] deviates as pair-wise correlations both have drawbacks. We develop an algorithm for adding noise, in a highly controlled manner, to general correlation matrices. In many instances, our method yields results which are superior to those obtained by simply simulating Gaussian data. Moreover, we demonstrate how our general algorithm can be tailored to a number of different correlation models. Using our results with a few different applications, we show that simulating correlation matrices can help assess statistical methodology.