2022
Article
Journal of Computational and Graphical Statistics

Nonparametric Subset Scanning for Detection of Heteroscedasticity

By: Charles R. Doss and Edward McFowland III

Format:Print
| Pages:11

Abstract

We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning techniques to efficiently identify the subset of covariates that are most “heteroscedastically relevant”. Through simulations and a real data example, we demonstrate that HSS is capable of detecting heteroscedasticity in a wide range of settings, including in cases where existing global tests lack power. Furthermore, the global power of our method compares favorably to methods such as the Breusch-Pagan test.

Keywords

Scan Statistics; Anomaly Detection; Regression; Model Diagnostics

Citation

Doss, Charles R., and Edward McFowland III. "Nonparametric Subset Scanning for Detection of Heteroscedasticity." Journal of Computational and Graphical Statistics 31, no. 3 (2022): 813–823.