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BIC extensions for order-constrained model selection
Mulder,Joris Raftery, A. E.
Mulder,Joris
Raftery, A. E.
Abstract
The Schwarz or Bayesian information criterion (BIC) is one of the most widely used tools for model comparison in social science research. The BIC, however, is not suitable for evaluating models with order constraints on the parameters of interest. This article explores two extensions of the BIC for evaluating order-constrained models, one where a truncated unit information prior is used under the order-constrained model and the other where a truncated local unit information prior is used. The first prior is centered on the maximum likelihood estimate, and the latter prior is centered on a null value. Several analyses show that the order-constrained BIC based on the local unit information prior works better as an Occam’s razor for evaluating order-constrained models and results in lower error probabilities. The methodology based on the local unit information prior is implemented in the R package “BICpack” which allows researchers to easily apply the method for order-constrained model selection. The usefulness of the methodology is illustrated using data from the European Values Study.
Description
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Mulder’s research was supported by a NWO Vidi grant (452-17-006). Raftery’s research was supported by NIH grants R01 HD054511 and R01 HD 070936 and by the Center for Advanced Study in the Behavioral Sciences at Stanford University.
Date
2022
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Publisher
Research Projects
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Keywords
BAYES FACTORS, Bayesian information criterion, European Values Study, HYPOTHESES, model selection, order constraints, truncated priors
Citation
Mulder, J & Raftery, A E 2022, 'BIC extensions for order-constrained model selection', Sociological Methods & Research, vol. 51, no. 2, pp. 471-498. https://doi.org/10.1177/0049124119882459
License
info:eu-repo/semantics/openAccess