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  "Title": "Moment Corrected Correlation",
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  "Author": "Yi-Hui Zhou",
  "Description": "A number of biomedical problems involve performing many\nhypothesis tests, with an attendant need to apply stringent\nthresholds. Often the data take the form of a series of\npredictor vectors, each of which must be compared with a single\nresponse vector, perhaps with nuisance covariates. Parametric\ntests of association are often used, but can result in\ninaccurate type I error at the extreme thresholds, even for\nlarge sample sizes. Furthermore, standard two-sided testing can\nreduce power compared to the doubled p-value, due to asymmetry\nin the null distribution. Exact (permutation) testing\napproaches are attractive, but can be computationally intensive\nand cumbersome. MCC is an approximation to exact association\ntesting of two vectors that is accurate and fast enough for\nstandard use in high-throughput settings, and can easily\nprovide standard two-sided or doubled p-values. Compatibility\nupdates by Renova.",
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