Reference : The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact i...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/10993/51742
The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact in Bayesian statistics
English
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]
Oct-2022
Computational Statistics and Data Analysis
174
Yes
[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.
http://hdl.handle.net/10993/51742

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
1-s2.0-S0167947321001869-main.pdfPublisher postprint1.33 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.