Comparing and weighting imperfect models using D-probabilities.

Journal Article (Journal Article)

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 comparing imperfect models, and in providing model weights to be used in model aggregation. D-probabilities avoid some of the disadvantages of Bayesian model probabilities, such as large sensitivity to prior choice, and tend to place higher weight on a greater diversity of models. In an application to linear model selection against a Gaussian process reference, we provide simple analytic forms for routine implementation and show that D-probabilities automatically penalize model complexity. Some asymptotic properties are described, and we provide interesting probabilistic interpretations of the proposed model weights. The framework is illustrated through simulation examples and an ozone data application.

Full Text

Duke Authors

Cited Authors

  • Li, M; Dunson, DB

Published Date

  • January 2020

Published In

Volume / Issue

  • 115 / 531

Start / End Page

  • 1349 - 1360

PubMed ID

  • 33716357

Pubmed Central ID

  • PMC7954220

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.2019.1611140


  • eng