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Abstract:
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We consider statistical inference of smooth functions of the unknown in a semi-low-dimensional approach to the analysis of high-dimensional data. In a general setting and a number of specific examples, we discuss regular and super-efficient sample size requirements for de-biasing regularized estimators. We also discuss the benefit of unlabeled data for the estimation of linear functionals in semi supervised linear regression.
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