Vector quantile regression beyond the specified case

This paper studies vector quantile regression (VQR), which models the dependence of a random vector with respect to a vector of explanatory variables with enough flexibility to capture the whole conditional distribution, and not only the conditional mean. The problem of vector quantile regression is...

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Main Authors: Carlier, Guillaume, Chernozhukov, Victor V, Galichon, Alfred
Other Authors: Massachusetts Institute of Technology. Department of Economics
Format: Article
Language:English
Published: Elsevier BV 2019
Online Access:https://hdl.handle.net/1721.1/122676
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author Carlier, Guillaume
Chernozhukov, Victor V
Galichon, Alfred
author2 Massachusetts Institute of Technology. Department of Economics
author_facet Massachusetts Institute of Technology. Department of Economics
Carlier, Guillaume
Chernozhukov, Victor V
Galichon, Alfred
author_sort Carlier, Guillaume
collection MIT
description This paper studies vector quantile regression (VQR), which models the dependence of a random vector with respect to a vector of explanatory variables with enough flexibility to capture the whole conditional distribution, and not only the conditional mean. The problem of vector quantile regression is formulated as an optimal transport problem subject to an additional mean-independence condition. This paper provides results on VQR beyond the specified case which had been the focus of previous work. We show that even beyond the specified case, the VQR problem still has a solution which provides a general representation of the conditional dependence between random vectors. Keywords: Duality; Optimal transport; Vector quantile regression
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spelling mit-1721.1/1226762022-09-23T13:17:56Z Vector quantile regression beyond the specified case Carlier, Guillaume Chernozhukov, Victor V Galichon, Alfred Massachusetts Institute of Technology. Department of Economics This paper studies vector quantile regression (VQR), which models the dependence of a random vector with respect to a vector of explanatory variables with enough flexibility to capture the whole conditional distribution, and not only the conditional mean. The problem of vector quantile regression is formulated as an optimal transport problem subject to an additional mean-independence condition. This paper provides results on VQR beyond the specified case which had been the focus of previous work. We show that even beyond the specified case, the VQR problem still has a solution which provides a general representation of the conditional dependence between random vectors. Keywords: Duality; Optimal transport; Vector quantile regression 2019-11-01T18:03:33Z 2019-11-01T18:03:33Z 2017-09 2019-10-21T18:13:08Z Article http://purl.org/eprint/type/JournalArticle 0047-259X https://hdl.handle.net/1721.1/122676 Carlier, Guillaume et al. "Vector quantile regression beyond the specified case." Journal of Multivariate Analysis, 161, (July 2017): 96-102 © 2017 The Authors. en http://dx.doi.org/10.1016/j.jmva.2017.07.003 Journal of Multivariate Analysis Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Elsevier
spellingShingle Carlier, Guillaume
Chernozhukov, Victor V
Galichon, Alfred
Vector quantile regression beyond the specified case
title Vector quantile regression beyond the specified case
title_full Vector quantile regression beyond the specified case
title_fullStr Vector quantile regression beyond the specified case
title_full_unstemmed Vector quantile regression beyond the specified case
title_short Vector quantile regression beyond the specified case
title_sort vector quantile regression beyond the specified case
url https://hdl.handle.net/1721.1/122676
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