We introduce a simple, interpretable strategy for making predictions on test data when the features of the test data are available at the time of model fitting. Our proposal—customized training— clusters the data to find training points close to each test point and then fits an l1-regularized model (lasso) separately in each training cluster. This approach combines the local adaptivity of k-nea...

The Dantzig selector (Candes and Tao, 2007) is a new approach that has been proposed for performing variable selection and model fitting on linear regression models. It uses an L1 penalty to shrink the regression coefficients towards zero, in a similar fashion to the Lasso. While both the Lasso and Dantzig selector potentially do a good job of selecting the correct variables, several researcher...

In generalized linear regression problems with an abundant number of features, lasso-type regularization which imposes an `-constraint on the regression coefficients has become a widely established technique. Crucial deficiencies of the lasso were unmasked when Zhou and Hastie (2005) introduced the elastic net. In this paper, we propose to extend the elastic net by admitting general nonnegative...

Yuan an Lin (2004) proposed the grouped LASSO, which achieves shrinkage and selection simultaneously, as LASSO does, but works on blocks of covariates. That is, the grouped LASSO provides a model where some blocks of regression coefficients are exactly zero. The grouped LASSO is useful when there are meaningful blocks of covariates such as polynomial regression and dummy variables from categori...

The Lasso is a popular model selection and estimation procedure for linear models that enjoys nice theoretical properties. In this paper, we study the Lasso estimator for fitting autoregressive time series models. We adopt a double asymptotic framework where the maximal lag may increase with the sample size. We derive theoretical results establishing various types of consistency. In particular,...

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...

A new estimator, named S-LASSO, is proposed for the coefficient function of Function-on-Function linear regression model. The S-LASSO estimator shown to be able increase interpretability model, by better locating regions where zero, and smoothly estimate non-zero values function. sparsity ensured a \textit{functional LASSO penalty}, which pointwise shrinks toward zero function, while smoothness...

The Lasso is a popular and computationally efficient procedure for automatically performing both variable selection and coefficient shrinkage on linear regression models. One limitation of the Lasso is that the same tuning parameter is used for both variable selection and shrinkage. As a result, it typically ends up selecting a model with too many variables to prevent over shrinkage of the regr...

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...