parameters with the Lasso and the adaptive Lasso. The parsimonious random walk allows the parameters to be modelled non … randomly.We characterize the finite sample properties of the Lasso by deriving upper bounds on the estimation and prediction … probability tending to one.We also provide conditions under which the adaptive Lasso is able to achieve perfectmodel selection. We …

to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain …

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

This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO … in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when … excluded. Next, non-asymptotic probabilities are given for the Adaptive LASSO to select the correct sign pattern (and hence the …

individual specific variables that all could potentially impact the retirement decision.We use variants of the Lasso and the … adaptive Lasso applied to logistic regression in order to uncover determinants of the retirement decision. To the best of our …

the estimation error of the Lasso under two different sets of conditions on the covariates as well as the error terms … constants. These results are then used to show that the Lasso can be consistent in even very large models where the number of … regressors increases at an exponential rate in the sample size. Conditions under which the Lasso does not discard any relevant …

We establish oracle inequalities for a version of the Lasso in high-dimensional fixed effects dynamic panel data models …

autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong …