Variable Selection for High-Dimensional Heteroscedastic Regression and Its Applications
针对高维线性异方差模型，提出两阶段变量选择算法，证明其选择一致性，并通过数值模拟和半导体制造中的缺陷工具识别验证有效性。
We are examining variable selection in high-dimensional linear heteroscedastic models. Drawing inspiration from the connection between the linear heteroscedastic function and the interaction model, we develop a two-stage algorithm to identify the relevant variables in the model mentioned above. We demonstrate the selection consistency of our proposed two-stage method and highlight its efficacy through numerical simulations. Furthermore, we leverage our method to pinpoint defective tools during the semiconductor manufacturing process.