Reference : The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact i...
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The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact in Bayesian statistics
Ley, Christophe mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >]
Ghaderinezhad, Fatemeh mailto [Ghent University > Department of Applied Mathematics, Computer Science and Statistics]
Serrien, Ben mailto [Vrije Universiteit Brussel - VUB > Experimental Anatomy Research Group]
Computational Statistics and Data Analysis
[en] Effective sample size ; Neutrality ; Prior distribution ; Vallender formula ; Wasserstein distance
[en] The prior distribution is a crucial building block in Bayesian analysis, and its choice will impact the subsequent inference. It is therefore important to have a convenient way to quantify this impact, as such a measure of prior impact will help to choose between two or more priors in a given situation. To this end a new approach, the Wasserstein Impact Measure (WIM), is introduced. In three simulated scenarios, the WIM is compared to two competitor prior impact measures from the literature, and its versatility is illustrated via two real datasets.

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