Simultaneous inference in general parametric models

被引:10540
作者
Hothorn, Torsten [1 ]
Bretz, Frank [2 ]
Westfall, Peter [3 ]
机构
[1] Univ Munich, Inst Stat, D-80539 Munich, Germany
[2] Novartis Pharma AG, CH-4002 Basel, Switzerland
[3] Texas Tech Univ, Lubbock, TX 79409 USA
关键词
adjusted p-values; multiple comparisons; multiple tests; multivariate normal distribution; robust statistics; simultaneous confidence intervals;
D O I
10.1002/bimj.200810425
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here.
引用
收藏
页码:346 / 363
页数:18
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