Confidence Sets Based on Sparse Estimators Are Necessarily Large Report as inadecuate




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Abstract

Confidence sets based on sparse estimators are shown to be large compared to more standard confidence sets, demonstrating that sparsity of an estimator comes at a substantial price in terms of the quality of the estimator. The results are set in a general parametric or semiparametric framework.



Item Type: MPRA Paper -

Original Title: Confidence Sets Based on Sparse Estimators Are Necessarily Large-

Language: English-

Keywords: sparse estimator, consistent model selection, post-model-selection estimator, penalized maximum likelihood, confidence set, coverage probability-

Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision TheoryC - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General-





Author: Pötscher, Benedikt M.

Source: https://mpra.ub.uni-muenchen.de/15087/







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