Estimation Stability with Cross Validation (ESCV)

Understand

Cross-validation (CV) is often used to select the regularization parameter in high dimensional problems.

• However, when applied to the sparse modeling method Lasso, CV leads to models that are unstable in high-dimensions, and consequently not suited for reliable interpretation.
• In this paper, we propose a model-free criterion ESCV based on a new estimation stability (ES) metric and CV.
• Our proposed ESCV finds a locally ES-optimal model smaller than the CV choice so that the it fits the data and also enjoys estimation stability property.