Inference and Individualized Prediction via Sparse Wrapper Algorithms

Thomas M. Menino Convention & Exhibition Center 

Sparse Wrapper Algorithms (SWAG) generate collections of sparse, competitive models, providing an alternative to single-model selection in high-dimensional settings. This talk presents recent advances in statistical testing, post-selection inference, and individualized prediction within the SWAG framework. A permutation-based test is introduced to assess whether SWAG captures meaningful structure beyond chance by examining departures from uniform variable selection under the null. For post-selection inference, George p-values are used to quantify variable importance while accounting for selection uncertainty. Individualized prediction is achieved by stacking predictions across SWAG-selected models, leveraging model diversity to improve predictive stability and performance. Simulation studies and applications illustrate valid inference and improved prediction, highlighting SWAG as a unified approach to testing, inference, and personalized prediction.

Sparse Wrapper Algorithm

Rashomon inference

individualized prediction

Model stacking

post-selection inference 

Section on Statistical Learning and Data Science