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. Supplementary materials for this article are available online.
|Original language||English (US)|
|Number of pages||11|
|Journal||Journal of Computational and Graphical Statistics|
|State||Published - 2022|
Bibliographical notePublisher Copyright:
© 2022 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
- Anomaly detection
- Model diagnostics
- Scan statistics