Speaker
Mr
Ali Charkhi
(KULeuven)
Description
Post-selection inference has been considered a crucial topic in data
analysis. In this article, we develop a new method to obtain correct inference after model selection by the Akaike's information criterion Akaike (1973) in linear regression models. Confidence intervals can be calculated by incorporating the randomness of the model selection in the distribution of the parameter estimators which act as pivotal quantities. Simulation results show the accuracy of the proposed method.
Primary author
Mr
Ali Charkhi
(KULeuven)
Co-author
Ms
Gerda Claeskens
(KULeuven)
