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DIC in variable selection

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  • Angelika van der Linde

Abstract

Model comparison is discussed from an information theoretic point of view. In particular the posterior predictive entropy is related to the target yielding DIC and modifications thereof. The adequacy of criteria for posterior predictive model comparison is also investigated depending on the comparison to be made. In particular variable selection as a special problem of model choice is formalized in different ways according to whether the comparison is a comparison across models or within an encompassing model and whether a joint or conditional sampling scheme is applied. DIC has been devised for comparisons across models. Its use in variable selection and that of other criteria is illustrated for a simulated data set.

Suggested Citation

  • Angelika van der Linde, 2005. "DIC in variable selection," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(1), pages 45-56, February.
  • Handle: RePEc:bla:stanee:v:59:y:2005:i:1:p:45-56
    DOI: 10.1111/j.1467-9574.2005.00278.x
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    2. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    3. David Cutts & Edward Fieldhouse, 2009. "What Small Spatial Scales Are Relevant as Electoral Contexts for Individual Voters? The Importance of the Household on Turnout at the 2001 General Election," American Journal of Political Science, John Wiley & Sons, vol. 53(3), pages 726-739, July.
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    6. Shriner, Daniel & Yi, Nengjun, 2009. "Deviance information criterion (DIC) in Bayesian multiple QTL mapping," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1850-1860, March.
    7. Piou, Cyril & Berger, Uta & Grimm, Volker, 2009. "Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework," Ecological Modelling, Elsevier, vol. 220(17), pages 1957-1967.
    8. Follett, Lendie & Naald, Brian Vander, 2023. "Heterogeneity in choice experiment data: A Bayesian investigation," Journal of choice modelling, Elsevier, vol. 46(C).
    9. Mohammed H. AbuJarad & Eman S. A. AbuJarad & Athar Ali Khan, 2022. "Bayesian Survival Analysis of Type I General Exponential Distributions," Annals of Data Science, Springer, vol. 9(2), pages 347-367, April.
    10. Amir Dezfouli & Kristi Griffiths & Fabio Ramos & Peter Dayan & Bernard W Balleine, 2019. "Models that learn how humans learn: The case of decision-making and its disorders," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-33, June.

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