Bootstrap-after-Bootstrap Model Averaging for Reducing Model Uncertainty in Model Selection for Air Pollution Mortality Studies | Zendy

Steven Roberts | Zendy; Michael A. Martin | Zendy

Open Access

Bootstrap-after-Bootstrap Model Averaging for Reducing Model Uncertainty in Model Selection for Air Pollution Mortality Studies

Author(s) -

Steven Roberts,

Michael A. Martin

Publication year - 2009

Publication title -

environmental health perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.257

H-Index - 282

eISSN - 1552-9924

pISSN - 0091-6765

DOI - 10.1289/ehp.0901007

Subject(s) - akaike information criterion , model selection , bootstrap model , statistics , standard error , selection (genetic algorithm) , variance (accounting) , bayesian probability , bayesian inference , econometrics , mean squared error , mathematics , computer science , boson , physics , accounting , particle physics , particle decay , artificial intelligence , business

Concerns have been raised about findings of associations between particulate matter (PM) air pollution and mortality that have been based on a single "best" model arising from a model selection procedure, because such a strategy may ignore model uncertainty inherently involved in searching through a set of candidate models to find the best model. Model averaging has been proposed as a method of allowing for model uncertainty in this context.

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