Xu Gongjun, Lin Lifeng, Wei Peng, Pan Wei
School of Statistics, University of Minnesota, Minneapolis, Minnesota, U.S.A. 55455.
Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, U.S.A. 55455.
Biometrika. 2016 Sep;103(3):609-624. doi: 10.1093/biomet/asw029. Epub 2017 Mar 18.
Several two-sample tests for high-dimensional data have been proposed recently, but they are powerful only against certain limited alternative hypotheses. In practice, since the true alternative hypothesis is unknown, it is unclear how to choose a powerful test. We propose an adaptive test that maintains high power across a wide range of situations, and study its asymptotic properties. Its finite sample performance is compared with existing tests. We apply it and other tests to detect possible associations between bipolar disease and a large number of single nucleotide polymorphisms on each chromosome based on a genome-wide association study dataset. Numerical studies demonstrate the superior performance and high power of the proposed test across a wide spectrum of applications.
最近已经提出了几种针对高维数据的双样本检验，但它们仅对某些有限的备择假设具有强大的功效。在实际应用中，由于真实的备择假设是未知的，因此不清楚如何选择一个功效强大的检验方法。我们提出了一种自适应检验，它在广泛的情况下都能保持较高的功效，并研究了其渐近性质。将其有限样本性能与现有检验进行了比较。我们将其与其他检验方法应用于基于全基因组关联研究数据集来检测双相情感障碍与每条染色体上大量单核苷酸多态性之间可能存在的关联。数值研究表明，在广泛的应用中，所提出的检验具有卓越的性能和较高的功效。
