A note on generalised information criteria for structured sparse models
提出一种考虑模型稀疏模式的广义信息准则，给出渐近与非渐近的模型选择结果，并展示其在群Lasso和低秩矩阵回归中用于选择正则化参数的有效性。
Summary We propose a generalised information criteria ( gic ) that accounts for sparsity pattern in the model. We obtain both asymptotic and nonasymptotic results for model selection. Moreover, we show that the gic is useful for selecting the regularisation parameter in regularised estimation in high‐dimensional scenarios. The results are illustrated in two examples: group LASSO in the context of generalised linear regressions and low‐rank matrix regression.