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Outlier detection in contingency tables using decomposable graphical models
Author(s) -
Lindskou Mads,
Eriksen Poul Svante,
Tvedebrink Torben
Publication year - 2020
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12407
Subject(s) - contingency table , outlier , mathematics , statistic , test statistic , graph , graphical model , statistics , anomaly detection , statistical hypothesis testing , null distribution
For high‐dimensional data, it is a tedious task to determine anomalies such as outliers. We present a novel outlier detection method for high‐dimensional contingency tables. We use the class of decomposable graphical models to model the relationship among the variables of interest, which can be depicted by an undirected graph called the interaction graph . Given an interaction graph, we derive a closed‐form expression of the likelihood ratio test (LRT) statistic and an exact distribution for efficient simulation of the test statistic. An observation is declared an outlier if it deviates significantly from the approximated distribution of the test statistic under the null hypothesis. We demonstrate the use of the LRT outlier detection framework on genetic data modeled by Chow–Liu trees.