Model selection via standard error adjusted adaptive lasso

Model selection via standard error adjusted adaptive lasso

The adaptive lasso is a model selection method shown to be both consistent in variable selection and asymptotically normal in coefficient estimation. The actual variable selection performance of the adaptive lasso depends on the weight used. It turns out that the weight assignment using the OLS estimate (OLS-adaptive lasso) can result in very poor performance when collinearity of the model matr...

Subset ARMA selection via the adaptive Lasso

Model selection is a critical aspect of subset autoregressive moving-average (ARMA) modelling. This is commonly done by subset selection methods, which may be computationally intensive and even impractical when the true ARMA orders of the underlying model are high. On the other hand, automatic variable selection methods based on regularization do not directly apply to this problem because the i...

Path consistent model selection in additive risk model via Lasso.

As a flexible alternative to the Cox model, the additive risk model assumes that the hazard function is the sum of the baseline hazard and a regression function of covariates. For right censored survival data when variable selection is needed along with model estimation, we propose a path consistent model selector using a modified Lasso approach, under the additive risk model assumption. We sho...

Consistent selection via the Lasso

In this article we investigate consistency of selection in regression models via the popular Lasso method. Here we depart from the traditional linear regression assumption and consider approximations of the regression function f with elements of a given dictionary of M functions. The target for consistency is the index set of those functions from this dictionary that realize the most parsimonio...

Model Clustering via Group Lasso