Uncertainty analysis plays a pivotal role in identifying the important parameters affecting building energy consumption and estimate their effects at the early design stages. In this work, we consider the adaptive Lasso for uncertainty analysis in building performance simulation. This procedure has several appealing features: (1) We can introduce a large number of possible physical and environm...

Regularized regression methods for linear regression have been developed the last few decades to overcome the flaws of ordinary least squares regression with regard to prediction accuracy. In this chapter, three of these methods (Ridge regression, the Lasso, and the Elastic Net) are incorporated into CATREG, an optimal scaling method for both linear and nonlinear transformation of variables in ...

We consider an iterated Lasso approach for variable selection and estimation in sparse, high-dimensional logistic regression models. In this approach, we use the Lasso (Tibshirani 1996) to obtain an initial estimator and reduce the dimension of the model. We then use the Lasso as the initial estimator in the adaptive Lasso (Zou 2006) to obtain the final selection and estimation results. We prov...

Adaptive lasso is a weighted `1 penalization method for simultaneous estimation and model selection. It has oracle properties of asymptotic normality with optimal convergence rate and model selection consistency. Instrumental variable selection has become the focus of much research in areas of application for which datasets with both strong and weak instruments are available. This paper develop...

We analyze a class of norms defined via an optimal interpolation problem involving the composition of norms and a linear operator. This construction, known as infimal postcomposition in convex analysis, is shown to encompass various of norms which have been used as regularizers in machine learning, signal processing, and statistics. In particular, these include the latent group lasso, the overl...