A Bayesian Approach for Identifying Multivariate Differences Between Groups

• Published in: Advances in Intelligent Data Analysis XIV Vol. 9385; pp. 275 - 285
• Main Authors: Sverchkov, Yuriy, Cooper, Gregory F.
• Format: Book Chapter Journal Article
• Language: English
• Published: Switzerland Springer International Publishing AG 01.10.2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Bayesian Network; Comparative Effectiveness Research; Image processing; Marginal Likelihood; Parameter Independence; Pattern recognition; Posterior Odds
• ISBN: 3319244647; 9783319244648
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-24465-5_24

We present a novel approach to the problem of detecting multivariate statistical differences across groups of data. The need to compare data in a multivariate manner arises naturally in observational studies, randomized trials, comparative effectiveness research, abnormality and anomaly detection scenarios, and other application areas. In such comparisons, it is of interest to identify statistical differences across the groups being compared. The approach we present in this paper addresses this issue by constructing statistical models that describe the groups being compared and using a decomposable Bayesian Dirichlet score of the models to identify variables that behave statistically differently between the groups. In our evaluation, the new method performed significantly better than logistic lasso regression in indentifying differences in a variety of datasets under a variety of conditions.