Multiple comparisons in the general linear model

被引:36
作者
Hsu, JC
Nelson, B
机构
[1] Ohio State Univ, Dept Stat, Columbus, OH 43210 USA
[2] Northwestern Univ, Dept Ind Engn & Management Sci, Evanston, IL 60208 USA
关键词
linear programming; quantile estimation; variance reduction;
D O I
10.2307/1390767
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Whereas multiple comparisons computations in a one-way model are well understood, multiple comparisons computations in a general linear model (GLM) are not. For models with the so-called "one-way structure," no new technique is needed beyond proper substitution of terms. Examples of designs that guarantee a one-way structure include variance balanced designs and orthogonal designs. For models without a one-way structure, more sophisticated computational techniques are needed. Approximations based on the probabilistic inequalities of Bonferroni, Sidak, and Slepian are too conservative. Even the second-order Hunter-Worsley inequality is rather conservative. The so-called factor analytic approximation is quite accurate for multiple comparison with a control (MCC) and multiple comparison with the best (MCB), but conditions for it to be conservative are not known. This article describes a highly accurate, deterministic, conservative approximation that is applicable to a popular class of general linear models, and a fast, stochastic, conservative approximation that is generally applicable.
引用
收藏
页码:23 / 41
页数:19
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