Statistical Science

How Principled and Practical Are Penalised Complexity Priors?

Christian P. Robert and Judith Rousseau

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Article information

Source
Statist. Sci. Volume 32, Number 1 (2017), 36-40.

Dates
First available in Project Euclid: 6 April 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1491465624

Digital Object Identifier
doi:10.1214/16-STS603

Citation

Robert, Christian P.; Rousseau, Judith. How Principled and Practical Are Penalised Complexity Priors?. Statist. Sci. 32 (2017), no. 1, 36--40. doi:10.1214/16-STS603. https://projecteuclid.org/euclid.ss/1491465624


Export citation

References

  • Barnard, J., McCulloch, R. and Meng, X.-L. (2000). Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Statist. Sinica 10 1281–1311.
  • Berger, J. O., Bernardo, J. M. and Sun, D. (2009). Natural induction: An objective Bayesian approach. Rev. R. Acad. Cienc. Exactas Fís. Nat., Ser. A Mat. 103 125–135.
  • Bernardo, J.-M. and Smith, A. F. M. (1994). Bayesian Theory. Wiley, Chichester.
  • Bochkina, N. A. and Green, P. J. (2014). The Bernstein–von Mises theorem and nonregular models. Ann. Statist. 42 1850–1878.
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. and Rubin, D. B. (2014). Bayesian Data Analysis, 3rd ed. CRC Press, Boca Raton, FL.
  • Johnson, V. and Rossell, D. (2010). On the use of non-local prior densities in Bayesian hypothesis tests. J. R. Stat. Soc. Ser. B. Stat. Methodol. 72 143–170.
  • Kass, R. and Wasserman, L. (1996). Formal rules of selecting prior distributions: A review and annotated bibliography. J. Amer. Statist. Assoc. 91 1343–1370.
  • Liseo, B. (2005). The elimination of nuisance parameters. In Handbook of Statistics 25 (D. Dey and C. Rao, eds.) 193–219. Elsevier/North-Holland, Amsterdam.
  • Lyne, A.-M., Girolami, M., Atchadé, Y., Strathmann, H. and Simpson, D. (2015). On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods. Statist. Sci. 30 443–467.
  • Martin, R. and Liu, C. (2015). Marginal inferential models: Prior-free probabilistic inference on interest parameters. J. Amer. Statist. Assoc. 110 1621–1631.
  • Robert, C. (2001). The Bayesian Choice, 2nd ed. Springer, New York.
  • Seaman, J. W. III, Seaman, J. W. Jr. and Stamey, J. D. (2012). Hidden dangers of specifying noninformative priors. Amer. Statist. 66 77–84.
  • Watson, J. and Holmes, C. (2016). Approximate models and robust decisions. Statist. Sci. 31 465–489.
  • Zabell, S. L. (1992). R. A. Fisher and the fiducial argument. Statist. Sci. 7 369–387.

See also

  • Main article: Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors.