摘要：Grouping structures arise naturally in many high dimensional statistical problems. Incorporation of grouping information can efficiently improve the statistical accuracy and model interpretability. In addition, nonnegative constraints are essential to cope with index tracking problems. This paper proposes the nonnegative hierarchical lasso with nonnegative constraints on the coefficients both in low dimensional setting and ultra high dimensional setting, which is capable of simultaneous selection at both the group and withingroup levels with overlap, namely the bi-level selection..
