Structural tensor-on-tensor regression with interaction effects and its application to a hot rolling process. Issue 5 (20th October 2022)

• Record Type: Journal Article
• Authors: Miao, Huihui; Wang, Andi; Li, Bing; Shi, Jianjun
• Is Part Of: Journal of quality technology. Volume 54:Issue 5(2022)
• Page Start: 547
• Page End: 560
• Publication Date: 2022-10-20
• Subjects: interaction effect -- regularization -- tensor-on-tensor regression; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2021.1973931
• Languages: English
• ISSNs: 0022-4065

Abstract

Abstract: This paper proposes a method of Structural Tensor-On-Tensosr regression considering the Interaction effects (STOTI). To alleviate the curse of dimensionality and resolve computational challenge, the STOTI method describes the specific structure of the main and interaction effect tensors indicated by the prior knowledge of the data using corresponding regularization terms on their appropriate modes. We designed an ADMM consensus algorithm to estimate these coefficient tensors. Extensive simulations and a real case study of the hot rolling process verified the superiority of the proposed method in terms of estimation and prediction accuracy.