Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations

  title={Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations},
  author={Xiaohan Yan and Jacob Bien},
  journal={Statistical Science},
Demanding sparsity in estimated models has become a routine practice in statistics. In many situations, we wish to require that the sparsity patterns attained honor certain problem-specific constraints. Hierarchical sparse modeling (HSM) refers to situations in which these constraints specify that one set of parameters be set to zero whenever another is set to zero. In recent years, numerous papers have developed convex regularizers for this form of sparsity structure, which arises in… 
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