We establish sign recovery consistency and l(infinity)-error bounds for the Lasso partial likelihood estimator under suitable and interpretable conditions, including mutual incoherence conditions. More importantly, we show that the conditions of the incoherence and bounds on the minimal non-zero ...
if the predictors that are not in the true model are “irrepresentable” (in a sense to be clarified) by predictors that are in the true model. Furthermore, simulations are carried out to provide insights and understanding of this result. Keywords: Lasso, Model Selection, Consistency 1. ...
We consider the least-square regression problem with regularization by a block 1-norm, i.e., a sum of Euclidean norms over spaces of dimensions larger than one. This problem, referred to as the group Lasso, extends the usual regularization by the 1-norm where all spaces have dimension one...
In this paper, we study the strong consistency and rates of convergence of the Lasso estimator. It is shown that when the error variables have a finite mean, the Lasso estimator is strongly consistent, provided the penalty parameter (say, λn) is of smaller order than the sample size (say...
Consistency of model selection hinges on the correlation between significant and insignificant predictors for "large p, small n" problems. Thus, Irrepresen... Y Gai,L Zhu,L Lin - 《Statistica Sinica》 被引量: 15发表: 2013年 Model selection via standard error adjusted adaptive lasso The adaptiv...
An important question in feature selection is whether a selection strategy recovers the "true" set of features, given enough data. We study this question in the context of the popular Least Absolute Shrinkage and Selection Operator (Lasso) feature selection strategy. In particular, we consider the...
sparse that the number of critical variables is fixed while dimensionality grows withn. The authors consider the model selection problem of lasso for this kind of data. The authors investigate both theoretical guarantees and simulations, and show that the lasso is robust for various kinds of data...
i.i.d assumption and a non i.i.d assumption which is natural in the context of collaborative filtering. As for the Lasso and the group Lasso, the necessary condition implies that such proce- dures do not always estimate the rank correctly; following the adaptive version of the Lasso and...
The adaptive Lasso method uses weighted penalties to provide consistent estimates of coefficients. 自适应 Lasso 方法使用加权罚值来提供一致的系数估计值。 ParaCrawl Corpus For consistency and comparison purposes, coefficient of variation is measured on both the "per pound" ratios, although it is co...
Nie, F., Wang, X., Jordan, M., Huang, H.: The constrained laplacian rank algorithm for graph-based clustering. In Proceedings of the AAAI conference on artificial intelligence (2016) Lu, C., Feng, J., Lin, Z., Yan, S.: Correlation adaptive subspace segmentation by trace lasso. In ...