In data sets with many predictors, algorithms for identifying a good subset of predic-tors are often used. Most such algorithms do not account for any relationships between predictors. For example,â€¦ (More)

In this paper, we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying smallâ€¦ (More)

Transactional network data arise in many fields. Although social network models have been applied to transactional data, these models typically assume binary relations between pairs of nodes. Weâ€¦ (More)

Some Risks in the Construction and Analysis of Supersaturated Designs B. Abraham a , H. Chipman b & K. Vijayan c a Department of Statistics and Actuarial Science and Institute for Improvement inâ€¦ (More)

In the analysis of robust design experiments, a model is typically t to experimental data, and then used to select levels of control variables that desensitize the response to uncontrollableâ€¦ (More)

Bayesian Additive Regression Trees (BART) is a Bayesian approach to flexible non-linear regression which has been shown to be competitive with the best modern predictive methods such as those basedâ€¦ (More)

Reshu Agarwal, Pritam Ranjan and Hugh Chipman Department of Mathematics and Statistics Acadia University, Wolfville, Nova Scotia, Canada Abstract: Classification of satellite images is a keyâ€¦ (More)

When using the K-nearest neighbors method, one often ignores uncertainty in the choice of K. To account for such uncertainty, Holmes and Adams (2002) proposed a Bayesian framework for K-nearestâ€¦ (More)

We begin with an example that will be used throughout the chapter.The data come from Sorlie et al. (2001). The goal of that article was to â€œclassify breast carcinomas based on variations in geneâ€¦ (More)