In many linear regression problems, explanatory variables are activated in groups or clusters; group lasso has been proposed for regression in such cases. This paper studies the nonasymptotic regression performance of group lasso using `1/`2 regularization for arbitrary (random or deterministic) design matrices. In particular, the paper establishes under a statistical prior on the set of nonzer...

Professors Lockhart, Taylor, Tibshirani and Tibshirani are to be congratulated for their innovative and valuable contribution to the important and timely problem of testing the significance of covariates for the Lasso. Since the invention of the Lasso in Tibshirani (1996) for variable selection, there has been a huge growing literature devoted to its theory and implementation, its extensions to...

The present paper analyzes the existence of a long-run relationship between the trade balance and exchange rate of Iran and South Korea by a new approach. It proposes an asymmetric co-integrating NARDL model by two positive and negative partial sum decompositions which can use mixed I (0) and I (1) variables by bounds testing approach. The proposed tests are based on calculated F –statistics an...

The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain scenarios where the lasso is inconsistent for variable selection. We then propose a new version of ...

We investigate the variable selection problem for Cox’s proportional hazards model, and propose a unified model selection and estimation procedure with desired theoretical properties and computational convenience. The new method is based on a penalized log partial likelihood with the adaptively-weighted L1 penalty on regression coefficients, and is named adaptive-LASSO (ALASSO) estimator. Inste...

Logistic models are studied as a tool to convert output from numerical weather forecasting systems (deterministic and ensemble) into probability forecasts for binary events. A logistic model obtains by putting the logarithmic odds ratio equal to a linear combination of the inputs. As any statistical model, logistic models will suffer from over-fitting if the number of inputs is comparable to th...

The performance of the Lasso is well understood under the assumptions of the standard sparse linear model with homoscedastic noise. However, in several applications, the standard model does not describe the important features of the data. This paper examines how the Lasso performs on a non-standard model that is motivated by medical imaging applications. In these applications, the variance of t...

We develop a framework for post-selection inference with the lasso. At the core of our framework is a result that characterizes the exact (non-asymptotic) distribution of linear combinations/contrasts of truncated normal random variables. This result allows us to (i) obtain honest confidence intervals for the selected coefficients that account for the selection procedure, and (ii) devise a test...

Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous largearea forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number ...