Finite representation of quantile sets for multivariate data via vector linear programming

03/27/2023
by   Andreas Löhne, et al.
0

A well-known result states that empirical quantiles for finitely distributed univariate random variables can be obtained by solving a linear program. We show in this short note that multivariate empirical quantiles can be obtained in a very similar way by solving a vector linear program. This connection provides a new approach for computing Tukey depth regions and more general cone quantile sets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/29/2022

A note on the equivalence between the conditional uncorrelation and the independence of random variables

It is well known that while the independence of random variables implies...
research
05/29/2019

From Halfspace M-depth to Multiple-output Expectile Regression

Despite the renewed interest in the Newey and Powell (1987) concept of e...
research
07/24/2019

Deep Generative Quantile-Copula Models for Probabilistic Forecasting

We introduce a new category of multivariate conditional generative model...
research
02/04/2019

Safe projections of binary data sets

Selectivity estimation of a boolean query based on frequent itemsets can...
research
06/21/2019

Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p,q) processes

In this note, we build upon the asymptotic theory for GARCH processes, c...
research
01/29/2020

On the behavior of extreme d-dimensional spatial quantiles under minimal assumptions

"Spatial" or "geometric" quantiles are the only multivariate quantiles c...
research
07/21/2021

H-Sets for Kernel-Based Spaces

The concept of H-sets as introduced by Collatz in 1956 was very useful i...

Please sign up or login with your details

Forgot password? Click here to reset