On Brownian Distance Covariance and High Dimensional Data.
On Brownian Distance Covariance and High Dimensional Data.
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DOI：
10.1214/09-aoas312
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发表时间：
2009-01-01
期刊：
影响因子：
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通讯作者：
Kosorok MR
中科院分区：
文献类型：
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作者：
Kosorok MR
我们简要地讨论了非常有趣的概念，布朗距离协方差开发和描述两种可能的扩展。第一个扩展是针对可以强制进入希尔伯特空间的高维数据，包括某些高通量筛选和功能数据设置。第二个扩展涉及非常简单的修改，可以在某些设置中产生更大的功率。我们赞扬Székely和Rizzo非常有趣的工作，并认识到这个一般性的想法有可能对统计学家评估数据依赖性的方式产生重大影响。
We discuss briefly the very interesting concept of Brownian distance covariance developed by and describe two possible extensions. The first extension is for high dimensional data that can be coerced into a Hilbert space, including certain high throughput screening and functional data settings. The second extension involves very simple modifications that may yield increased power in some settings. We commend Székely and Rizzo for their very interesting work and recognize that this general idea has potential to have a large impact on the way in which statisticians evaluate dependency in data.