2R) model, where a hierarchical variable selection is developed to identify informative signals and further screen significant elements within the selected signals, and a multitask learning is devised to exploit the smoothness nature of surface response and the similarity structure among a series of subregression tasks. Our SSF²R model is concisely formulated as a convex problem with an efficient iterative algorithm derived to obtain the global optimum. Moreover, our quality prediction can be performed dynamically during an ongoing manufacturing process when only partial observations of the signal predictors are available. The superiority of our proposed method is validated by numerical simulations and a real case study in the semiconductor industry.">

Sparse and Structured Function-on-Function Quality Predictive Modeling by Hierarchical Variable Selection and Multitask Learning (original) (raw)

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