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- W790284814 abstract "Quantifying the uncertainty in penalized regression under group sparsity is an important open question. We establish, under a high-dimensional scaling, the asymptotic validity of a modified parametric bootstrap method for the group lasso, assuming a Gaussian error model and mild conditions on the design matrix and the true coefficients. Simulation of bootstrap samples provides simultaneous inferences on large groups of coefficients. Through extensive numerical comparisons, we demonstrate that our bootstrap method performs much better than popular competitors, highlighting its practical utility. The theoretical result is generalized to other block norm penalization and sub-Gaussian errors, which further broadens the potential applications." @default.
- W790284814 created "2016-06-24" @default.
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- W790284814 date "2015-07-05" @default.
- W790284814 modified "2023-09-23" @default.
- W790284814 title "Uncertainty Quantification Under Group Sparsity" @default.
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- W790284814 doi "https://doi.org/10.48550/arxiv.1507.01296" @default.
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