Gupta, M. (2014) An evolutionary Monte Carlo algorithm for Bayesian block clustering of data matrices. Computational Statistics and Data Analysis, 71, 375- 391. (doi: 10.1016/j.csda.2013.07.006)
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Abstract
In many applications, it is of interest to simultaneously cluster row and column variables in a data set, identifying local subgroups within a data matrix that share some common characteristic. When a small set of variables is believed to be associated with a set of responses, block clustering or biclustering is a more appropriate technique to use compared to one-dimensional clustering. A flexible framework for Bayesian model-based block clustering, that can determine multiple block clusters in a data matrix through a novel and efficient evolutionary Monte Carlo-based methodology, is proposed. The performance of this methodology is illustrated through a number of simulation studies and an application to data from genome-wide association studies.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Gupta, Professor Mayetri |
| Authors: | Gupta, M. |
| Subjects: | Q Science > QA Mathematics |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics |
| Journal Name: | Computational Statistics and Data Analysis |
| Publisher: | Elsevier |
| ISSN: | 1932-1864 |
| ISSN (Online): | 1872-7352 |
| Published Online: | 15 July 2013 |
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