A new approach to clustering multivariate data, based on a multilevel linear mixed model, is proposed. A key feature of the model is that observations from the same cluster are correlated, because they share cluster-specific random effects. The inclusion of cluster-specific random effects allows...

We study a general algorithm to improve the accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We shrink the centroids of clusters toward the overall mean of all data using a James-Stein-type adjustment, and then the James-Stein shrinkage estimators...

Cluster analysis attempts to group data objects into homogeneous clusters on the basis of the pairwise dissimilarities among the objects. When the data contain noise, we might consider performing a smoothing operation, either on the data themselves or on the dissimilarities, before implementing...

We develop a novel approach for testing treatment effects in high-throughput data. Most previous works on this topic focused on testing for differences between the means, but recently it has been recognized that testing for differential variation is probably as important. We take it a step...

In this article, we analyze voter choice data from six U.S. presidential elections for evidence of religious identity and sociodemographic effects on voter choice. Copyright (c) 2009 by the Southwestern Social Science Association.

In almost all documented applications, the normal-based Lugannani and Rice (1980) formula provides an extremely accurate approximation to tail probabilities. However, it would be wrong to conclude that this formula is always reliable. In the present paper, we consider an example, the first...