Comparing and Weighting Imperfect Models Using D-Probabilities

We propose a new approach for assigning weights to models using a divergence-based method (D-probabilities), relying on evaluating parametric models relative to a nonparametric Bayesian reference using Kullback-Leibler divergence. D-probabilities are useful in goodness-of-fit assessments, in compari...

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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 115; no. 531; pp. 1349 - 1360
Main Authors:	Li, Meng, Dunson, David B.
Format:	Journal Article
Language:	English
Published:	United States Taylor & Francis 02.07.2020 Taylor & Francis Ltd
Subjects:	Asymptotic properties Atmospheric ozone Bayesian analysis Bayesian theory Computer simulation Gaussian process Gibbs posterior Goodness of fit Kullback-Leibler divergence Linear analysis linear models M-open Model aggregation Model selection Morality normal distribution ozone Property Regression analysis Simulation Statistical methods Statistics Weighting Gaussian process Model selection Nonparametric Bayes M-open Kullback-Leibler divergence Model aggregation Gibbs posterior Posterior probabilities
ISSN:	0162-1459, 1537-274X, 1537-274X
Online Access:	Get full text
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