Zooming in on Anomalies: An Approach to Detecting Cellwise Outliers

Music City Center 

In many modern applications, there is a growing need to identify specific problematic entries within a dataset, referred to as cellwise outliers. These differ from the more commonly studied casewise outliers, which focus on identifying entire rows as anomalous. While numerous statistical methods exist for detecting casewise outliers (also called anomaly detection or exception mining), relatively few methods address the challenge of pinpointing problematic values within individual observations. We propose a Mahalanobis distance-based chi-squared test statistic designed to detect cellwise outliers. Using Monte Carlo simulations, we evaluate the performance of our method against existing approaches across datasets generated from various multivariate distributions. Our results demonstrate that the proposed method is computationally efficient and often outperforms competing techniques in accurately identifying cellwise outliers under a wide range of conditions.

cellwise outliers

anomaly detection

exception mining

multivariate data

Mahalanobis' distance 

Section on Statistical Learning and Data Science