Abstract
This paper proposes a new automatic forward model selection method based on leave-one-out (LOO) cross-validation and a fast forward recursive algorithm. It can automatically select a sparse model by incrementally minimizing a LOO test error from a pool of candidate model terms, without the need to specify an additional termination criterion. By defining a proper regression context, the computational efficiency is ensured. A numerical example demonstrates the effectiveness of the proposed method.
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