Open Access
February 1999 Stepwise multiple test procedures and control of directional errors
H. Finner
Ann. Statist. 27(1): 274-289 (February 1999). DOI: 10.1214/aos/1018031111

Abstract

One of the most difficult problems occurring with stepwise multiple test procedures for a set of two-sided hypotheses is the control of direc-tional errors if rejection of a hypothesis is accomplished with a directional decision. In this paper we generalize a result for so-called step-down procedures derived by Shaffer to a large class of stepwise or closed multiple test procedures. In a unifying way we obtain results for a large class of order statistics procedures including step-down as well as step-up procedures (Hochberg, Rom), but also a procedure of Hommel based on critical values derived by Simes. Our method of proof is also applicable in situations where directional decisions are mainly based on conditionally independent $t$-statistics. A closed $F$-test procedure applicable in regression models with orthogonal design, the modified $S$-method of Scheffé applicable in the Analysis of Variance and Fisher’s LSD-test for the comparison of three means will be considered in more detail.

Citation

Download Citation

H. Finner. "Stepwise multiple test procedures and control of directional errors." Ann. Statist. 27 (1) 274 - 289, February 1999. https://doi.org/10.1214/aos/1018031111

Information

Published: February 1999
First available in Project Euclid: 5 April 2002

zbMATH: 0978.62057
MathSciNet: MR1701111
Digital Object Identifier: 10.1214/aos/1018031111

Subjects:
Primary: 62F03 , 62J15
Secondary: 62C99 , 62F07

Keywords: Closed multiple test procedure , closure principle , directional error , familywise error rate , F-test , Multiple comparisons , Multiple hypotheses testing , multiple level of significance , step-down procedure , step-up procedure , stepwise multiple test procedure , totally positive of order 3 , type III error , Unimodality , variation diminishing property

Rights: Copyright © 1999 Institute of Mathematical Statistics

Vol.27 • No. 1 • February 1999
Back to Top