A0252
Title: Variable selection for high-dimensional heteroscedastic regression and its applications
Authors: Hsueh-Han Huang - Academia Sinica (Taiwan) [presenting]
Abstract: Variable selection is examined in high-dimensional linear heteroscedastic models. Drawing inspiration from the connection between the linear heteroscedastic function and the interaction model, a two-stage algorithm is developed to identify the relevant variables in the model mentioned above. The selection consistency of the proposed two-stage method is demonstrated, and its efficacy is highlighted through numerical simulations. Furthermore, the method is leveraged to pinpoint defective tools during the semiconductor manufacturing process.
