Reference : From robust tests to Bayes-like posterior distributions
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Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/10993/47741
From robust tests to Bayes-like posterior distributions
English
Baraud, Yannick mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >]
26-Jul-2021
No
[en] Bayes procedure ; Robustness ; Hellinger distance ; Total variation distance.
[en] In the Bayes paradigm and for a given loss function, we propose the construction of a new type of posterior distributions for estimating the law of an n-sample. The loss functions we have in mind are based on the total variation distance, the Hellinger distance as well as some 𝕃j-distances. We prove that, with a probability close to one, this new posterior distribution concentrates its mass in a neighbourhood of the law of the data, for the chosen loss function, provided that this law belongs to the support of the prior or, at least, lies close enough to it. We therefore establish that the new posterior distribution enjoys some robustness properties with respect to a possible misspecification of the prior, or more precisely, its support. For the total variation and squared Hellinger losses, we also show that the posterior distribution keeps its concentration properties when the data are only independent, hence not necessarily i.i.d., provided that most of their marginals are close enough to some probability distribution around which the prior puts enough mass. The posterior distribution is therefore also stable with respect to the equidistribution assumption. We illustrate these results by several applications. We consider the problems of estimating a location parameter or both the location and the scale of a density in a nonparametric framework. Finally, we also tackle the problem of estimating a density, with the squared Hellinger loss, in a high-dimensional parametric model under some sparcity conditions. The results established in this paper are non-asymptotic and provide, as much as possible, explicit constants.
Researchers
http://hdl.handle.net/10993/47741
https://arxiv.org/abs/2107.12011
H2020 ; 811017 - SanDAL - ERA Chair in Mathematical Statistics and Data Science for the University of Luxembourg

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