The classical multiple comparison procedures aim to control the probability of type I error in families of comparisons. When some hypotheses are not true, the classical family wise error rate controlling procedures tend to have less power and less error rate than the significance level. To improve the power, an approach is to estimate the number of true hypotheses in multiple testing. A nonparametric approach based on the McNemar test is presented. The proposed estimate has the advantage of direct computation. It is not like the other estimation methods that need to compute iteratively. The criterion is illustrated with an example from a microarray data set. Finally, a simulation study is conducted to evaluate the performance of proposed procedure and the methods proposed by reviewed literatures. The simulation results show that the proposed procedure has smaller mean square error than the other methods.
Keywords: Family wise error rate; Multiple testing; McNemar test
Biography: Prof. Mi-Chia Ma obtained Ph.D. degree in Applied Mathematics with major in Statistics under the guidance of Prof Anne Chao at National Tsing Hwa University in 1992. She is working as an associate professor at Department of Statistics, National Cheng Kung University since 1992. Her current research topics include analysis and applications of micro array data, estimation of diversity in ecology statistics. She is a newcomer in participation of ISI World Statistics Congress.