Multiple Comparisons: Theory and Methods

Multiple Comparisons introduces simultaneous statistical inference and covers the theory and techniques for all-pairwise comparisons, multiple comparisons with the best, and multiple comparisons with a control. The author describes confidence intervals methods and stepwise exposes abuses and misconceptions, and guides readers to the correct method for each problem. Discussions also include the connections with bioequivalence, drug stability, and toxicity studies Real data sets analyzed by computer software packages illustrate the applications presented.

Jason C. Hsu is a Professor in the Department of Statistics at The Ohio State University.

Preface
Introduction to simultaneous statistical inference	p. 1
Classification of multiple comparison methods	p. 24
Multiple comparisons with a control	p. 43
Multiple comparisons with the best	p. 81
All-pairwise comparisons	p. 119
Abuses and misconceptions in multiple comparisons	p. 175
Multiple comparisons in the general linear model	p. 181
A Some useful probabilistic inequalities	p. 227
B Some useful geometric lemmas	p. 231
C Sample size computations	p. 236
D Accessing computer codes	p. 243
E Tables of critical values	p. 245
Bibliography	p. 258
Author index	p. 271
Subject index	p. 274
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Multiple Comparisons: Theory and Methods

Summary

Author Biography

Table of Contents

Supplemental Materials