Asymptotic permutation tests in general factorial designs
Asymptotic permutation tests in general factorial designs
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DOI：
10.1111/rssb.12073
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发表时间：
2015-03-01
影响因子：
5.8
通讯作者：
Konietschke, Frank
中科院分区：
文献类型：
--
作者：
Pauly, Markus;Brunner, Edgar;Konietschke, Frank
在一般的析因设计中，没有假设同方差或特定的误差分布，著名的Wald型统计量是一个简单的渐近有效的程序。然而，它是众所周知的，它遭受了一个穷人的有限样本近似，因为收敛到其（2）极限分布是相当缓慢的。随着因子水平数量的增加，情况变得更糟。本文的目的是提高小样本行为的沃尔德型统计，保持其适用性的一般设置交叉或分层嵌套设计，通过应用修改后的排列方法。特别是，它表明，这种方法近似的零分布的沃尔德型统计量不仅在零假设下，但也根据替代产生一个渐近有效的置换检验，甚至是精确的下exchangecutive。最后，它的小样本行为进行了比较，在一个广泛的模拟研究与竞争程序。
In general factorial designs where no homoscedasticity or a particular error distribution is assumed, the well-known Wald-type statistic is a simple asymptotically valid procedure. However, it is well known that it suffers from a poor finite sample approximation since the convergence to its (2) limit distribution is quite slow. This becomes even worse with an increasing number of factor levels. The aim of the paper is to improve the small sample behaviour of the Wald-type statistic, maintaining its applicability to general settings as crossed or hierarchically nested designs by applying a modified permutation approach. In particular, it is shown that this approach approximates the null distribution of the Wald-type statistic not only under the null hypothesis but also under the alternative yielding an asymptotically valid permutation test which is even finitely exact under exchangeability. Finally, its small sample behaviour is compared with competing procedures in an extensive simulation study.