Treed Variance
提出一种名为“树状方差”的数据驱动方法，通过递归分割数据并用CART剪枝确定最优树大小，对线性回归中的误差方差进行分段常数建模，并自然检验同方差性。
This article proposes a data-driven tree method, called “treed variance” (TV), to model heteroscedasticity in linear regression. Specifically, we use a score test statistic to recursively bisect data into heterogenous groups, and then adopt the pruning methodology of CART to determine the best tree size. The proposed method provides not only a piecewise constant modeling of the error variance, but also facilitates a natural check of homoscedasticity. We assess the performance of the TV method via simulation studies and illustrate its use with an empirical example.