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A Bayesian Approach for Identifying Multivariate Differences Between Groups

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Advances in Intelligent Data Analysis XIV (IDA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9385))

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Abstract

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.

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Acknowledgments

This research was supported by grant IIS-0911032 from the National Science Foundation and grant T15 LM007359 from the National Library of Medicine.

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Authors and Affiliations

  1. University of Wisconsin—Madison, Madison, WI, USA

    Yuriy Sverchkov

  2. University of Pittsburgh, Pittsburgh, PA, USA

    Gregory F. Cooper

Authors
  1. Yuriy Sverchkov
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  2. Gregory F. Cooper
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Corresponding author

Correspondence to Yuriy Sverchkov .

Editor information

Editors and Affiliations

  1. Université de Saint-Etienne, Saint-Etienne, France

    Elisa Fromont

  2. Intelligent Systems Lab, University of Bristol Intelligent Systems Lab, Bristol, United Kingdom

    Tijl De Bie

  3. Informatics Section, Katholieke Universiteit Leuven, Leuven, Belgium

    Matthijs van Leeuwen

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Sverchkov, Y., Cooper, G.F. (2015). A Bayesian Approach for Identifying Multivariate Differences Between Groups. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_24

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  • DOI: https://doi.org/10.1007/978-3-319-24465-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24464-8

  • Online ISBN: 978-3-319-24465-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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