【摘 要】
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Model selection and sparse recovery are two important problems for which many regularization methods have been proposed.We study the properties of regulariz
【机 构】
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University of Southern Califomia
【出 处】
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2009 International Conference on Financial Statistics and Fi
论文部分内容阅读
Model selection and sparse recovery are two important problems for which many regularization methods have been proposed.We study the properties of regularization methods in both problems under the unified framework of regularized least squares with concave penalties.For model selection,we establish conditions under which a regularized least squares estimator enjoys a no asymptotic property,called the weak oracle property,where the dimensionality can grow exponentially with sample size.
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