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Outlier detection for multivariate categorical data

Author
Puig, X.; Ginebra, J.
Type of activity
Journal article
Journal
Quality and reliability engineering international
Date of publication
2018
Volume
34
Number
7
First page
1400
Last page
1412
DOI
https://doi.org/10.1002/qre.2339 Open in new window
Repository
http://hdl.handle.net/2117/121784 Open in new window
URL
https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.2339 Open in new window
Abstract
The detection of outlying rows in a contingency table is tackled from a Bayesian perspective, by adapting the framework adopted by Box and Tiao for normal models to multinomial models with random effects. The solution assumes a 2–component mixture model of 2 multinomial continuous mixtures for them, one for the nonoutlier rows and the second one for the outlier rows. The method starts by estimating the distributional characteristics of nonoutlier rows, and then it does cluster analysis to iden...
Citation
Puig, X., Ginebra, J. Outlier detection for multivariate categorical data. "Quality and reliability engineering international", 2018, vol. 34, núm. 7, p. 1400-1412.
Group of research
ADBD - Analysis of Complex Data for Business Decisions

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