Multiple Comparisons: Theory and MethodsCRC Press, 01.02.1996 - 296 Seiten 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. |
Inhalt
Classification of multiple comparison methods | 24 |
Multiple comparisons with a control | 43 |
Multiple comparisons with the best | 81 |
Allpairwise comparisons | 119 |
Abuses and misconceptions in multiple comparisons | 175 |
Multiple comparisons in the general linear model | 181 |
A Some useful probabilistic inequalities | 227 |
B Some useful geometric lemmas | 231 |
Accessing computer codes | 243 |
| 258 | |
Author index | 271 |
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Häufige Begriffe und Wortgruppen
all-pairwise comparisons ANCOVA balanced one-way model Bonferroni inequality confidence set confident directions method confident inequalities method constrained MCB coverage probability critical value degrees of freedom denote distribution Dunnett's method effect is better error rate estimators example experimentwise error rate F-test Hayter incorrect assertion ith treatment larger treatment effect least one incorrect Lemma lower confidence bounds maxj≠i MCC method MINITAB multiple comparison methods multiple range test n₁ null hypothesis one-sided MCC p-values P(UE pairwise parameters of primary PDIFF practical equivalence primary interest PROC GLM Proof quantile range statistics rejected sample means SAS system Scheffé's method Section simultaneous confidence intervals size-a statistics stepdown method Studentized residuals Suppose Table Theorem treatment means Tukey Tukey-Kramer method Tukey's method two-sided MCC upper confidence bounds variance variance-covariance matrix δι μ₁ με μι μκ σ²
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