We present a new continuity correction to the P value for local maxima of a statistical parametric map that bridges the gap between small FWHM, when the Bonferroni correction is accurate, and large FWHM, when random field theory is accurate. The new method, based on discrete local maxima, is always an upper bound (like the Bonferroni), but lower and hence more accurate for large FWHM, without i...

In this paper, we discuss the Schur convexity, Schur geometric convexity and Schur harmonic convexity of the generalized geometric Bonferroni mean. Some inequalities related to the generalized geometric Bonferroni mean are established to illustrate the applications of the obtained results.

This study examined the relative family-wise error (FWE) rate and statistical power of multivariate permutation tests (MPTs), Bonferroni-adjusted alpha, and uncorrected-alpha tests of significance for bivariate associations. Although there are many previous applications of MPTs, this is the first to apply it to testing bivariate associations. Electrocortical studies were selected as an example ...

By extending the Bonferroni mean operator to the intuitionistic fuzzy situation, we introduce a new kind of aggregating operator: the induced generalized weighted Bonferroni mean(I-GWBM) operator. The I-GWBM operator includes many famous aggregation operators for intuitionistic fuzzy sets(IFSs) as special cases. Then, we generalize the I-GWBM operator by using quasi-arithmetic means and propose...

we derive whole series of new integral inequalities of the hardy-type, with non-conjugate exponents. first, we prove and discuss two equivalent general inequa-li-ties of such type, as well as their corresponding reverse inequalities. general results are then applied to special hardy-type kernel and power weights. also, some estimates of weight functions and constant factors are obtained. ...

Introduction Bonferroni Pruning Pruning is a common technique to avoid over tting in decision trees. Most pruning techniques do not account for one important factor | multiple comparisons. Multiple comparisons occur when an induction algorithm examines several candidate models and selects the one that best accords with the data. Making multiple comparisons produces incorrect inferences about mo...