【摘 要】
：
A class of thresholding-based iterative selection procedures (TISP) is proposed for model selection and shrinkage.People have long before noticed the weakne
【机 构】
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Florida State Unviversity
【出 处】
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2009 International Conference on Financial Statistics and Fi
论文部分内容阅读
A class of thresholding-based iterative selection procedures (TISP) is proposed for model selection and shrinkage.People have long before noticed the weakness of the convex L 1-constraint (or the soft-thresholding) in wavelets and have designed many different forms of nonconvex penalties to increase model sparsity and accuracy.But for a no orthogonal regression matrix,there is great difficulty in both investigating the performance in theory and solving the problem in computation.Starting from the thresholding rules rather than penalty functions,TISP provides a simple and efficient way to tackle this problem.
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