The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.

We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of ℓ₁-regularized regression (Lasso) for this problem, ...

In linear regression with functional predictors and scalar responses, it may be advantageous, particularly if the function is thought to contain features at many scales, to restrict the coefficient ...