Inference after model selection and averaging via confidence distributions and curves KU Leuven
Model selection and model averaging are common practices to find the best model that explains the observed data. When the working model is selected using data-driven methods and the same data are used for inference about population parameters, guarantees of classical inference techniques might not hold anymore. This dissertation discusses ways of producing valid inference for post-selection and for model averaged estimators via confidence ...