Multicarving for high-dimensional post-selection inference

Ranking and Selection in High Dimensional Inference

Post - Selection Inference

It is common practice in statistical data analysis to perform datadriven variable selection and derive statistical inference from the resulting model. Such inference enjoys none of the guarantees that classical statistical theory provides for tests and confidence intervals when the model has been chosen a priori. We propose to produce valid “post-selection inference” by reducing the problem to ...

Valid Post-Selection Inference

It is common practice in statistical data analysis to perform data-driven variable selection and derive statistical inference from the resulting model. Such inference enjoys none of the guarantees that classical statistical theory provides for tests and confidence intervals when the model has been chosen a priori. We propose to produce valid “post-selection inference” by reducing the problem to...

Post - Selection Inference Online Appendix

B.1 The Full Model Interpretation of Parameters. In the full model interpretation, coefficients always have the fixed meaning as full model parameters. Variable selection then means setting some coefficient estimates to zero, and these estimates always exist for all predictors, irrespective of whether they are selected or deselected. The full model interpretation of parameters is appropriate, f...

POST - SELECTION INFERENCE By Richard

It is common practice in statistical data analysis to perform datadriven variable selection and derive statistical inference from the resulting model. Such inference enjoys none of the guarantees that classical statistical theory provides for tests and confidence intervals when the model has been chosen a priori. We propose to produce valid “post-selection inference” by reducing the problem to ...