Chapter title |
A Bayesian Approach for Identifying Multivariate Differences Between Groups
|
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Chapter number | 24 |
Book title |
Advances in Intelligent Data Analysis XIV
|
Published in |
Advances in intelligent data analysis. International Symposium on Intelligent Data Analysis, October 2015
|
DOI | 10.1007/978-3-319-24465-5_24 |
Pubmed ID | |
Book ISBNs |
978-3-31-924464-8, 978-3-31-924465-5
|
Authors |
Yuriy Sverchkov, Gregory F. Cooper |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 2 | 25% |
Researcher | 2 | 25% |
Student > Ph. D. Student | 1 | 13% |
Professor > Associate Professor | 1 | 13% |
Student > Doctoral Student | 1 | 13% |
Other | 0 | 0% |
Unknown | 1 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 4 | 50% |
Mathematics | 2 | 25% |
Psychology | 1 | 13% |
Engineering | 1 | 13% |