منابع مشابه
Degrees of freedom in lasso problems
We derive the degrees of freedom of the lasso fit, placing no assumptions on the predictor matrix X. Like the well-known result of Zou et al. (2007), which gives the degrees of freedom of the lasso fit when X has full column rank, we express our result in terms of the active set of a lasso solution. We extend this result to cover the degrees of freedom of the generalized lasso fit for an arbitr...
متن کاملDegrees of Freedom of the Group Lasso
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overdetermined linear regression model. Such a regularization is known to promote sparsity patterns structured as nonoverlapping groups of coefficients. Our main contribution provides a local parameterization of the solution with respect to the observations. As a byproduct, we give an unbiased estimat...
متن کاملOn the “degrees of Freedom” of the Lasso By
We study the effective degrees of freedom of the lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degrees of freedom of the lasso—a conclusion that requires no special assumption on the predictors. In addition, the unbiased estimator is shown to be asymptotically consistent. With these results on h...
متن کاملOn the “Degrees of Freedom” of the Lasso
We study the degrees of freedom of the Lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of non-zero coefficients is an unbiased estimate for the degrees of freedom of the Lasso—a conclusion that requires no special assumption on the predictors. Our analysis also provides mathematical support for a related conjecture by Efron et al. (2004). As an applica...
متن کاملThe degrees of freedom of the Group Lasso for a General Design
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverlapping blocks, and where only a few of them are assumed to be active. In such a situation, the group Lasso is an attractive method for variable selection since it promotes sparsity of the groups. We study the sensitivity of any group Lasso solution to the observations and provide its precise loca...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2012
ISSN: 0090-5364
DOI: 10.1214/12-aos1003