Group LASSO is widely used to enforce the structural sparsity, which achieves the sparsity at the inter-group level. In this paper, we propose a new formulation called “exclusive group LASSO”, which brings out sparsity at intra-group level in the context of feature selection. The proposed exclusive group LASSO is applicable on any feature structures, regardless of their overlapping or non-overl...

In this paper, we discuss a parsimonious approach to estimation of high-dimensional covariance matrices via the modified Cholesky decomposition with lasso. Two different methods are proposed. They are the equiangular and equi-sparse methods. We use simulation to compare the performance of the proposed methods with others available in the literature, including the sample covariance matrix, the b...

In this paper we use adaptive lasso estimator select between relevant and irrelevant instruments in heteroskedastic and non Gaussian data. To do so limit theory of Zou (2006) is extended from univariate iid case. Next, it is shown that adaptive lasso estimator can achieve near minimax risk bound even in the case of heteroskedastic data. To achieve that a new proof is used that benefits from Ste...

Lasso is a popular method for variable selection in regression. Much theoretical understanding has been obtained recently on its model selection or sparsity recovery properties under sparse and homoscedastic linear regression models. Since these standard model assumptions are often not met in practice, it is important to understand how Lasso behaves under nonstandard model assumptions. In this ...

abstract background: pabon lasso model was applied to assess the relative performance of hospitals affiliated to kurdistan university of medical sciences (kums) before and after the implementation of health sector evolution plan (hsep) in iran. methods: this cross-sectional study was carried out in 11 public hospitals affiliated to kums in 2015. twelve months before and after the implementation...

2 3 Preface Many classification procedures are based on variable selection methodologies. This master thesis concentrates on continuous variable selection procedures based on the shrinkage principle. Generally, we would like to find sparse prediction rules for multi-class classification problems such that in increases the prediction accuracy but also the interpretability of the obtained predict...

The lasso peptide microcin J25 is known to hijack the siderophore receptor FhuA for initiating internalization. Here, we provide what is to our knowledge the first structural evidence on the recognition mechanism, and our biochemical data show that another closely related lasso peptide cannot interact with FhuA. Our work provides an explanation on the narrow activity spectrum of lasso peptides ...

In this paper we revisit the risk bounds of the lasso estimator in the context of transductive and semi-supervised learning. In other terms, the setting under consideration is that of regression with random design under partial labeling. The main goal is to obtain user-friendly bounds on the off-sample prediction risk. To this end, the simple setting of bounded response variable and bounded (hi...