Statistical Science

Model Uncertainty First, Not Afterwards

Ingrid Glad and Nils Lid Hjort

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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.

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

First available in Project Euclid: 19 January 2017

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Envelopes Kullback–Leibler distance local neighbourhoods model robustness


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

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