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Discussion of "Multivariate quantiles and multiple-output regression quantiles: From $L_1$ optimization to halfspace depth" by M. Hallin, D. Paindaveine and M. Siman [arXiv:1002.4486]

Statistics Theory · Mathematics 2010-02-25 Linglong Kong , Ivan Mizera

Discussion of "Multivariate quantiles and multiple-output regression quantiles: From $L_1$ optimization to halfspace depth" by M. Hallin, D. Paindaveine and M. Siman [arXiv:1002.4486]

Statistics Theory · Mathematics 2010-02-25 Ying Wei

Rejoinder to "Multivariate quantiles and multiple-output regression quantiles: From $L_1$ optimization to halfspace depth" by M. Hallin, D. Paindaveine and M. Siman [arXiv:1002.4486]

Statistics Theory · Mathematics 2010-02-25 Marc Hallin , Davy Paindaveine , Miroslav Šiman

A new multivariate concept of quantile, based on a directional version of Koenker and Bassett's traditional regression quantiles, is introduced for multivariate location and multiple-output regression problems. In their empirical version,…

Statistics Theory · Mathematics 2010-02-25 Marc Hallin , Davy Paindaveine , Miroslav Šiman

Despite the renewed interest in the Newey and Powell (1987) concept of expectiles in fields such as econometrics, risk management, and extreme value theory, expectile regression---or, more generally, M-quantile regression---unfortunately…

Statistics Theory · Mathematics 2019-05-31 Abdelaati Daouia , Davy Paindaveine

This paper is written for a Festschrift in honour of Professor Marc Hallin and it proposes some developments on quantile regression. We connect our investigation to Marc's scientific production and we present some theoretical and…

Applications · Statistics 2023-09-12 Manon Felix , Davide La Vecchia , Hang Liu , Yiming Ma

We are most grateful to all discussants for their positive comments and many thought-provoking questions. In addition, the discussants provide a number of useful leads into various areas of the literatures on time series, forecasting and…

The use of quantiles to obtain insights about multivariate data is addressed. It is argued that incisive insights can be obtained by considering directional quantiles, the quantiles of projections. Directional quantile envelopes are…

Methodology · Statistics 2014-12-01 Linglong Kong , Ivan Mizera

This is the rejoinder for discussion of "Multinomial Inverse Regression for Text Analysis", Journal of the American Statistical Association 108, 2013.

Applications · Statistics 2013-08-09 Matt Taddy

A number of topics in analysis are discussed, with emphasis on basic principles. There is some overlap with "Elements of linear and real analysis" (arXiv:math/0108030), with numerous changes in content and presentation since then.

Classical Analysis and ODEs · Mathematics 2011-11-22 Stephen Semmes

This paper focuses on generalizing quantiles from the ordering point of view. We propose the concept of partial quantiles, which are based on a given partial order. We establish that partial quantiles are equivariant under order-preserving…

Statistics Theory · Mathematics 2011-05-31 Alexandre Belloni , Robert L. Winkler

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

Statistics Theory · Mathematics 2012-11-06 Emmanuel J. Candés , Mahdi Soltanolkotabi

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

Statistics Theory · Mathematics 2012-11-06 Zhao Ren , Harrison H. Zhou

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

Statistics Theory · Mathematics 2012-11-06 Christophe Giraud , Alexandre Tsybakov

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

Statistics Theory · Mathematics 2012-11-06 Martin J. Wainwright

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

Statistics Theory · Mathematics 2012-11-06 Steffen Lauritzen , Nicolai Meinshausen

Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

Statistics Theory · Mathematics 2012-11-06 Ming Yuan

In this paper, we first revisit the Koenker and Bassett variational approach to (univariate) quantile regression, emphasizing its link with latent factor representations and correlation maximization problems. We then review the multivariate…

General Economics · Economics 2021-02-26 Guillaume Carlier , Victor Chernozhukov , Gwendoline De Bie , Alfred Galichon

This is a discussion of the paper "Modeling an Augmented Lagrangian for Improved Blackbox Constrained Optimization," (Gramacy, R.~B., Gray, G.~A., Digabel, S.~L., Lee, H.~K.~H., Ranjan, P., Wells, G., and Wild, S.~M., Technometrics, 61,…

Optimization and Control · Mathematics 2015-07-30 Warren Hare , Jason Loeppky , Brian Williams

Discussion of "Multivariate Bayesian Logistic Regression for Analysis of Clinical Trial Safety Issues" by W. DuMouchel [arXiv:1210.0385].

Methodology · Statistics 2012-10-03 Don Berry
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