- Statist. Sci.
- Volume 31, Number 4 (2016), 490-494.
Model Uncertainty First, Not Afterwards
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.
Statist. Sci., Volume 31, Number 4 (2016), 490-494.
First available in Project Euclid: 19 January 2017
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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
- Main article: Approximate Models and Robust Decisions.