@article{203de813bb164dfcace1c9b02b043820,
title = "Estimation of a sparse group of sparse vectors",
abstract = "We consider estimating a sparse group of sparse normal mean vectors, based on penalized likelihood estimation with complexity penalties on the number of nonzero mean vectors and the numbers of their significant components, which can be performed by a fast algorithm. The resulting estimators are developed within a Bayesian framework and can be viewed as maximum a posteriori estimators. We establish their adaptive minimaxity over a wide range of sparse and dense settings. A simulation study demonstrates the efficiency of the proposed approach, which successfully competes with the sparse group lasso estimator.",
keywords = "Adaptive minimaxity, Complexity penalty, Maximum a posteriori rule, Sparsity, Thresholding",
author = "Felix Abramovich and Vadim Grinshtein",
note = "Funding Information: Both authors were supported by the Israel Science Foundation. We are grateful to Ofir Harari for his assistance in running simulation examples and Saharon Rosset for fruitful discussions.",
year = "2013",
month = jun,
doi = "10.1093/biomet/ass082",
language = "אנגלית",
volume = "100",
pages = "355--370",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "2",
}