Nonparametric Subset Scanning for Detection of Heteroscedasticity
提出异方差性子集扫描方法，用于识别回归中违反同方差假设的协变量，通过模拟和实例证明其检测能力优于传统全局检验。
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.