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Activity Number: 392
Type: Invited
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #314409
Title: Bayesian Inference for Quantile Estimation
Author(s): Catia Scricciolo* and Judith Rousseau
Companies: Bocconi University and Université Paris-Dauphine/CREST
Keywords: Adaptive estimation ; Credibility regions ; Deconvolution ; Frequentist coverage ; Quantile function
Abstract:

Adaptive Bayesian quantile estimation in deconvolution problems with unknown error distribution is studied. The objective is to estimate quantiles of a distribution from indirect observations that are additively corrupted by error measurements. Quantile estimation in deconvolution is an example of non-linear functional estimation in ill-posed inverse problems: the problem can in fact be translated into the problem of estimating the cumulative distribution function. We are interested in quantifying uncertainty by credibility regions whose frequentist interpretation is investigated.

Authors who are presenting talks have a * after their name.

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