Choose on a grid by K-fold cross-validation, comparing the same held-out prediction loss and optionally applying the one-standard-error rule for a simpler model. A genuinely untouched test set can then estimate final prediction error.
Ordinary model-based intervals after selecting nonzero coefficients ignore selection and are generally invalid. Valid approaches include Debiased Lasso or a selective-inference procedure under its assumptions, sample splitting followed by an unpenalized refit and inference on the independent half, or a bootstrap that repeats both tuning and fitting and is interpreted with care near the nonsmooth zero threshold.

Articles by others on the same topic (0)

There are currently no matching articles.