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Title	Bayesian High Dimensional Multivariate Regression with Shrinkage Priors
Creator	Malay Ghosh
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-04-08T16:21
Description	We consider sparse Bayesian estimation in the classical multivariate linear regression model with p regressors and q response variables. In univariate Bayesian linear regression with a single response y, shrinkage priors which can be expressed as scale-mixtures of normal densities are a popular approach for obtaining sparse estimates of the coefficients. In this paper, we extend the use of these priors to the multivariate case to estimate a p times q coefficients matrix B. Our method can be used for any sample size n and any dimension p. Moreover, we show that the posterior distribution can consistently estimate B even when p grows at nearly exponential rate with the sample size n. Concentration inequalities are proved and our results are illustrated through simulation and data analysis.
Extent	56.0 minutes
Subject	Mathematics Statistics
Geographic Location	Banff (Alta.)
Type	Moving Image
FileFormat	video/mp4
Language	eng
Notes	Author affiliation: University of Florida
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2019-10-06
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0383294
URI	http://hdl.handle.net/2429/71835
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
AggregatedSourceRepository	DSpace

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