Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted p-values are required as input for multiple testing procedures. Such situations prevail when testing for differential expression of genes in microarray studies. The Cramer-von Mises two-sample test, based on a certain L2-distance beten two empirical distribution functions, is a distribution-free test that has proven itself as a good choice. A numerical algorithm is available for computing quantiles of the sampling distribution of the Cramer-von Mises test statistic in finite samples. Hover, the computation is very time-and space-consuming. An L1 counterpart of the Cramer-von Mises test represents an appealing alternative. In this work,  present an efficient algorithm for computing exact quantiles of the L1-distance test statistic. The performance and por of the L1-distance test are compared with those of the Cramer-von Mises and two other classical tests, using both simulated data and a large set of microarray data on childhood leukemia. The L1-distance test appears to be nearly as porful as its L2 counterpart. The lor computational intensity of the L1-distance test allows computation of exact quantiles of the null distribution for larger sample sizes than is possible for the Cramer-von Mises test.
['Yuanhui Xiao', 'Alexander Y. Gordon', 'Andrei Yakovlev']
I am writing a research paper on a idea The L 1 -version of the Cramér-von mises test for two-sample comparisons in microarray data analysis
[{"from": "human", "value": "I am writing a research paper on a idea The L 1 -version of the Cram\u00e9r-von mises test for two-sample comparisons in microarray data analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted p-values are required as input for multiple testing procedures. Such situations prevail when testing for differential expression of genes in microarray studies. The Cramer-von Mises two-sample test, based on a certain L2-distance beten two empirical distribution functions, is a distribution-free test that has proven itself as a good choice. A numerical algorithm is available for computing quantiles of the sampling distribution of the Cramer-von Mises test statistic in finite samples. Hover, the computation is very time-and space-consuming. An L1 counterpart of the Cramer-von Mises test represents an appealing alternative. In this work,  present an efficient algorithm for computing exact quantiles of the L1-distance test statistic. The performance and por of the L1-distance test are compared with those of the Cramer-von Mises and two other classical tests, using both simulated data and a large set of microarray data on childhood leukemia. The L1-distance test appears to be nearly as porful as its L2 counterpart. The lor computational intensity of the L1-distance test allows computation of exact quantiles of the null distribution for larger sample sizes than is possible for the Cramer-von Mises test."}]