Vector quantile regression beyond correct specification

By Guillaume Carlier, Victor Chernozhukov, Alfred Galichon. 2016

8 citations recorded by the source corpus.

This paper studies vector quantile regression (VQR), which is a way to model the dependence of a random vector of interest with respect to a vector of explanatory variables so 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 a new set of results on VQR beyond the case with correct specification which had been the focus of previous work. First, we show that even under misspecification, the VQR problem still has a solution which provides a general representation of the conditional dependence between random vectors. Second, we provide a detailed comparison with the classical approach of Koenker and Bassett in the case when the dependent variable is univariate and we show that in that case, VQR is equivalent to classical quantile regression with an additional monotonicity constraint.

Subfields: Applied Mathematics, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Causal Generative Neural Networks (2017, methodology): “By induction on the topological order of G , taking inspiration from [Carlier et al. , 2016].”
Deep Generative Quantile-Copula Models for Probabilistic Forecasting (2019, background): “Carlier et al, 2016 built a connection between vector quantile regression and the optimal transport problem, but mostly from theoretical aspects.”
Computational Optimal Transport (2018, methodology): “optimal transport can be used to define quantile functions in arbitrary dimensions, which is in turn useful for applications to quantile regression problems [Carlier et al., 2016].”