On Model Selection Consistency of Lasso
Abstract
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used as a computationally feasible alternative to model selection. Therefore it is important to study Lasso for model selection purposes.
Cite
Text
Zhao and Yu. "On Model Selection Consistency of Lasso." Journal of Machine Learning Research, 2006.Markdown
[Zhao and Yu. "On Model Selection Consistency of Lasso." Journal of Machine Learning Research, 2006.](https://mlanthology.org/jmlr/2006/zhao2006jmlr-model/)BibTeX
@article{zhao2006jmlr-model,
title = {{On Model Selection Consistency of Lasso}},
author = {Zhao, Peng and Yu, Bin},
journal = {Journal of Machine Learning Research},
year = {2006},
pages = {2541-2563},
volume = {7},
url = {https://mlanthology.org/jmlr/2006/zhao2006jmlr-model/}
}