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Model Averaging

Part of the SpringerBriefs in Statistics Book Series

DOI: 10.1007/978-3-662-58541-2

ISBN: 978-3-662-58540-5

Publisher: Springer Berlin Heidelberg - Springer Journals

Jan 18, 2019

Available

Why Model Averaging?

Fletcher, David

Pages: 1 –29

[Model averaging is a means of allowing for model uncertainty in estimation which can provide better estimates and more reliable confidence intervals than model selection. We illustrate its use via examples involving real data, discuss when it is likely to be useful, and compare the frequentist...
Available

Bayesian Model Averaging

Fletcher, David

Pages: 31 –55

[We provide an overview of Bayesian model averaging (BMA), starting with a summary of the mathematics associated with classical BMA, including the calculation of posterior model probabilities and the choice of priors for both the models and the model parameters. We also consider prediction-based...
Available

Frequentist Model Averaging

Fletcher, David

Pages: 57 –97

[We provide an overview of frequentist model averaging. For point estimation, we consider different methods for selecting the model weights, including those based on AIC, bagging, weighted AIC, stacking and focussed methods. For interval estimation, we consider Wald, MATA and percentile-bootstrap...
Available

Summary and Future Directions

Fletcher, David

Pages: 99 –101

[We provide an overview of the key ideas and results in Bayesian and frequentist model averaging, and suggest directions for future research.]

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