An improved Bonferroni procedure for multiple tests of significance

@article{Simes1986AnIB,
  title={An improved Bonferroni procedure for multiple tests of significance},
  author={Robert John Simes},
  journal={Biometrika},
  year={1986},
  volume={73},
  pages={751-754}
}
  • R. Simes
  • Published 1 December 1986
  • Business
  • Biometrika
SUMMARY A modification of the Bonferroni procedure for testing multiple hypotheses is presented. The method, based on the ordered p-values of the individual tests, is less conservative than the classical Bonferroni procedure but is still simple to apply. A simulation study shows that the probability of a type I error of the procedure does not exceed the nominal significance level, a, for a variety of multivariate normal and multivariate gamma test statistics. For independent tests the procedure… 

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References

SHOWING 1-5 OF 5 REFERENCES

A Simple Sequentially Rejective Multiple Test Procedure

This paper presents a simple and widely ap- plicable multiple test procedure of the sequentially rejective type, i.e. hypotheses are rejected one at a tine until no further rejections can be done. It

An improved Bonferroni inequality and applications

SUMMARY We present an improved Bonferroni inequality which gives an upper bound for the probability of the union of an arbitrary sequence of events. The bound is constructed in terms of the joint

Simultaneous Statistical Inference

1 Introduction.- 1 Case of two means.- 2 Error rates.- 2.1 Probability of a nonzero family error rate.- 2.2 Expected family error rate.- 2.3 Allocation of error.- 3 Basic techniques.- 3.1 Repeated

On Probabilities of Rectangles in Multivariate Student Distributions: Their Dependence on Correlations

where X = (X1, *, Xj) is as before, while Si = (;P= 1 Z,)+, i = 1 '*, k, where ZV = (Z1l , Zkv), v = 1, *',p, is a random sample of p vectors, which are mutually independent and independent of X, and