Multiple comparison procedures with the average forkdependent normal means

Abstract

To cite this article: Shu-Fei Wu (1999) Multiple comparison procedures with the average for k dependent normal means, Journal of Statistics and Management Systems, 2:1, 73-95, ABSTRACTIn Wu and Chen [8], multiple comparison procedures with the average fer k independent normal means when variances are known or unknown are proposed. The exact critical values are obtained when common variances are known or unknown and sample sizes are all equal. However, when k norm'll populations are dependent, the problem becomes more complicated. In this article, we consider the multiple comparison procedures with the average for k normal means when popUlations are not independent by simultaneous confidence interval and subset selection approaches. These procedures have broad applicability in identifying better-than-the-average, worse-than-the.average and not-much·difference·from the average stocks in Dow.Jone industrial stock market and 'it can also select a subset which includes all better.than-the-average treatments within its own group in experimental design if treatments are not independent. These procedures can be a very usefi.!l screening procedure especially when k is large. The comparison of the efficiency between approximation results by Bonferroni inequality ane! simulation results by Monte Carlo method has been studied and it shows Bonferroni approximates are efficient for equal correlation cases. An example of eight biggest mutual ftmds in the United States is also given to illustrate the implementation of these multiple comparison procedures with the average.