Statistical Science

Model Uncertainty First, Not Afterwards

Ingrid Glad and Nils Lid Hjort

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Abstract

Watson and Holmes propose ways of investigating robustness of statistical decisions by examining certain neighbourhoods around a posterior distribution. This may partly amount to ad hoc modelling of extra uncertainty. Instead of creating neighbourhoods around the posterior a posteriori, we argue that it might be more fruitful to model a layer of extra uncertainty first, in the model building process, and then allow the data to determine how big the resulting neighbourhoods ought to be. We develop and briefly illustrate a general strategy along such lines.

Article information

Source
Statist. Sci. Volume 31, Number 4 (2016), 490-494.

Dates
First available in Project Euclid: 19 January 2017

Permanent link to this document
https://projecteuclid.org/euclid.ss/1484816573

Digital Object Identifier
doi:10.1214/16-STS559

Keywords
Envelopes Kullback–Leibler distance local neighbourhoods model robustness

Citation

Glad, Ingrid; Hjort, Nils Lid. Model Uncertainty First, Not Afterwards. Statist. Sci. 31 (2016), no. 4, 490--494. doi:10.1214/16-STS559. https://projecteuclid.org/euclid.ss/1484816573


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References

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  • Hjort, N. L. (2003). Topics in non-parametric Bayesian statistics. In Highly Structured Stochastic Systems (P. J. Green, N. L. Hjort and S. Richardson, eds.). Oxford Statist. Sci. Ser. 27 455–487. Oxford Univ. Press, Oxford. With discussion.
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  • Schweder, T. and Hjort, N. L. (2016). Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions. Cambridge Univ. Press, Cambridge.

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