Statistical Science

Rejoinder: Approximate Models and Robust Decisions

James Watson and Chris Holmes

Full-text: Open access

Article information

Source
Statist. Sci., Volume 31, Number 4 (2016), 516-520.

Dates
First available in Project Euclid: 19 January 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1484816579

Digital Object Identifier
doi:10.1214/16-STS596

Mathematical Reviews number (MathSciNet)
MR3598732

Zentralblatt MATH identifier
06946244

Citation

Watson, James; Holmes, Chris. Rejoinder: Approximate Models and Robust Decisions. Statist. Sci. 31 (2016), no. 4, 516--520. doi:10.1214/16-STS596. https://projecteuclid.org/euclid.ss/1484816579


Export citation

References

  • Ahmadi-Javid, A. (2011). An information-theoretic approach to constructing coherent risk measures. In IEEE International Symposium on Information Theory Proceedings (ISIT) 2125–2127. IEEE, New York.
  • Artzner, P., Delbaen, F., Eber, J.-M. and Heath, D. (1999). Coherent measures of risk. Math. Finance 9 203–228.
  • Berry, S. M., Carlin, B. P., Lee, J. J. and Müller, P. (2011). Bayesian Adaptive Methods for Clinical Trials. CRC Press, Boca Raton, FL.
  • Bissiri, P. G., Holmes, C. C. and Walker, S. G. (2016). A general framework for updating belief distributions. J. R. Stat. Soc. Ser. B. Stat. Methodol. To appear.
  • Box, G. E. P. (1980). Sampling and Bayes’ inference in scientific modelling and robustness. J. Roy. Statist. Soc. Ser. A 143 383–430.
  • Carota, C., Parmigiani, G. and Polson, N. G. (1996). Diagnostic measures for model criticism. J. Amer. Statist. Assoc. 91 753–762.
  • Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. Q. J. Econ. 643–669.
  • Grünwald, P. and van Ommen, T. (2014). Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it. Preprint. Available at arXiv:1412.3730.
  • Hansen, L. P. (2014). Nobel lecture: Uncertainty outside and inside economic models. J. Polit. Econ. 122 945–987.
  • Hansen, L. P. and Sargent, T. J. (2008). Robustness. Princeton University Press, Princeton, NJ.
  • Maccheroni, F., Marinacci, M. and Rustichini, A. (2006). Ambiguity aversion, robustness, and the variational representation of preferences. Econometrica 74 1447–1498.
  • Simpson, D. P., Rue, H., Martins, T. G., Riebler, A. and Sørbye, S. H. (2014). Penalising model component complexity: A principled, practical approach to constructing priors. Preprint. Available at arXiv:1403.4630.
  • Spiegelhalter, D. J., Freedman, L. S. and Parmar, M. K. B. (1994). Bayesian approaches to randomized trials. J. Roy. Statist. Soc. Ser. A 157 357–416.
  • Watson, J., Nieto-Barajas, L. and Holmes, C. (2016). Characterising variation of nonparametric random probability models using the Kullback–Leibler divergence. Statistics. To appear. Available at arXiv:1411.6578.
  • Whittle, P. (1990). Risk-Sensitive Optimal Control. Wiley, Chichester.

See also