Post-Selection Inference
Arun Kumar Kuchibhotla, John E. Kolassa, Todd A. Kuffner
Abstract
We discuss inference after data exploration, with a particular focus on inference after model or variable selection. We review three popular approaches to this problem: sample splitting, simultaneous inference, and conditional selective inference. We explain how each approach works and highlight its advantages and disadvantages. We also provide an illustration of these post-selection inference approaches.
Topics & Concepts
InferenceComputer scienceSelection (genetic algorithm)Machine learningArtificial intelligenceFiducial inferencePredictive inferenceFocus (optics)Frequentist inferenceBayesian inferenceOpticsPhysicsBayesian probabilityStatistical Methods and InferenceAdvanced Statistical Methods and ModelsGaussian Processes and Bayesian Inference