In a multiple testing problem where one is willing to tolerate a few false rejections, procedure controlling the familywise error rate (FWER) can potentially be improved in terms of its ability to detect false null hypotheses by generalizing it to control the $k$-FWER, the probability of falsely rejecting at least $k$ null hypotheses, for some fixed $k>1$. Simes' test for testing the intersection null hypothesis is generalized to control the $k$-FWER weakly, that is, under the intersection null hypothesis, and Hochberg's stepup procedure for simultaneous testing of the individual null hypotheses is generalized to control the $k$-FWER strongly, that is, under any configuration of the true and false null hypotheses. The proposed generalizations are developed utilizing joint null distributions of the $k$-dimensional subsets of the $p$-values, assumed to be identical. The generalized Simes' test is proved to control the $k$-FWER weakly under the multivariate totally positive of order two (MTP$_2$) condition [J. Multivariate Analysis 10 (1980) 467--498] of the joint null distribution of the $p$-values by generalizing the original Simes' inequality. It is more powerful to detect $k$ or more false null hypotheses than the original Simes' test when the $p$-values are independent. A stepdown procedure strongly controlling the $k$-FWER, a version of generalized Holm's procedure that is different from and more powerful than [Ann. Statist. 33 (2005) 1138--1154] with independent $p$-values, is derived before proposing the generalized Hochberg's procedure. The strong control of the $k$-FWER for the generalized Hochberg's procedure is established in situations where the generalized Simes' test is known to control its $k$-FWER weakly.

Published in at http://dx.doi.org/10.1214/009053607000000550 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)