On the asymptotic properties of the group lasso estimator for linear models
Copyright policy )On the asymptotic properties of the group lasso estimator for linear models
We establish estimation and model selection consistency, prediction and estimation bounds and persistence for the group-lasso estimator and model selector proposed by Yuan and Lin (2006) for least squares problems when the covariates have a natural grouping structure. We consider the case of a fixed-dimensional parameter space with increasing sample size and the double asymptotic scenario where the model complexity changes with the sample size.
- Carnegie Mellon University United States
model selection, Linear regression; mixed models, group-lasso, sparsity, Least Squares, Probability theory, persistence, Group-Lasso, Persistence, least squares, 62J05, oracle inequalities, Oracle Inequalities, 62F12, Sparsity, Model Selection, Asymptotic properties of parametric estimators, Statistics not elsewhere classified
model selection, Linear regression; mixed models, group-lasso, sparsity, Least Squares, Probability theory, persistence, Group-Lasso, Persistence, least squares, 62J05, oracle inequalities, Oracle Inequalities, 62F12, Sparsity, Model Selection, Asymptotic properties of parametric estimators, Statistics not elsewhere classified
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