(automatic general-to-specific selection) and LASSO (?1-norm regularization). In a simulation study, we show the performance of …

(automatic general-to-specific selection) and LASSO (ℓ1-norm regularization). In a simulation study, we show the performance of …

of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully …

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time …

In this paper we show the validity of the adaptive LASSO procedure in estimating stationary ARDL(p,q) models with GARCH … innovations. We show that, given a set of initial weights, the adaptive Lasso selects the relevant variables with probability … variables beforehand. Finally, we show that the LASSO estimator can be used to construct the initial weights. The performance of …

simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal …

of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable …

of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully …

In this paper we show the validity of the adaptive LASSO procedure in estimating stationary ARDL(p,q) models with GARCH … innovations. We show that, given a set of initial weights, the adaptive Lasso selects the relevant variables with probability … variables beforehand. Finally, we show that the LASSO estimator can be used to construct the initial weights. The performance of …

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time …