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Activity Number: 69 - Highlights of the Canadian Journal of Statistics
Type: Invited
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: SSC (Statistical Society of Canada)
Abstract #316782
Title: Post Model-Fitting Exploration via a 'Next-Door' Analysis
Author(s): Robert Tibshirani and Leying Guan*
Companies: Stanford Univ and Yale Univ
Keywords: model selection; lasso; cross-validation

We propose a simple method for evaluating the model that has been chosen by an adaptive regression procedure, our main focus being the lasso. This procedure deletes each chosen predictor and refits the lasso to get a set of models that are "close" to the one chosen, referred to as "base model". If the deletion of a predictor leads to significant deterioration in the model's predictive power, the predictor is called indispensable; otherwise, the nearby model is called acceptable and can serve as a good alternative to the base model. This provides both an assessment of the predictive contribution of each variable and a set of alternative models that may be used in place of the chosen model.

Authors who are presenting talks have a * after their name.

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