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The order-restricted association model: Two estimation algorithms and issues in testing 总被引:1,自引:0,他引:1
This paper presents a row-column (RC) association model in which the estimated row and column scores are forced to be in agreement
with an a priori specified ordering. Two efficient algorithms for finding the order-restricted maximum likelihood (ML) estimates
are proposed and their reliability under different degrees of association is investigated by a simulation study. We propose
testing order-restricted RC models using a parametric bootstrap procedure, which turns out to yield reliablep values, except for situations in which the association between the two variables is very weak. The use of order-restricted
RC models is illustrated by means of an empirical example.
Francisca Galindo performed this research as a part of her PhD. dissertation project at Tilburg University. 相似文献
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