Variable Selection for High-Dimensional Heteroscedastic Regression and Its Applications

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. Supplementary materials for this article are available online.