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Viewpoint: model selection uncertainty, pre‐specification, and model averaging
Author(s) -
Bornkamp Björn
Publication year - 2015
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1671
Subject(s) - model selection , selection (genetic algorithm) , computer science , specification , econometrics , statistics , mathematics , artificial intelligence , machine learning
Scientific progress in all empirical sciences relies on selecting models and performing inferences from selected models. Standard statistical properties (e.g., repeated sampling coverage probability of confidence intervals) cannot be guaranteed after a model selection. This viewpoint reviews this dilemma, puts the role that pre‐specification can play into perspective and illustrates model averaging as a way to relax the problem of model selection uncertainty. Copyright © 2015 John Wiley & Sons, Ltd.