Modeling sign concordance of quantile regression residuals with multiple outcomes

by   Silvia Columbu, et al.

Quantile regression permits describing how quantiles of a scalar response variable depend on a set of predictors. Because a unique definition of multivariate quantiles is lacking, extending quantile regression to multivariate responses is somewhat complicated. In this paper, we describe a simple approach based on a two-step procedure: in the first step, quantile regression is applied to each response separately; in the second step, the joint distribution of the signs of the residuals is modeled through multinomial regression. The described approach does not require a multidimensional definition of quantiles, and can be used to capture important features of a multivariate response and assess the effects of covariates on the correlation structure. We apply the proposed method to analyze two different datasets.



page 1

page 2

page 3

page 4


Semiparametric model averaging for high dimensional conditional quantile prediction

In this article, we propose a penalized high dimensional semiparametric ...

Noncrossing structured additive multiple-output Bayesian quantile regression models

Quantile regression models are a powerful tool for studying different po...

Canonical Regression Quantiles with application to CEO compensation and predicting company performance

In using multiple regression methods for prediction, one often considers...

Multivariate prediction of mixed, multilevel, sequential outcomes arising from in vitro fertilisation

In vitro fertilization (IVF) comprises a sequence of interventions conce...

Calibrated Multiple-Output Quantile Regression with Representation Learning

We develop a method to generate predictive regions that cover a multivar...

Joint Quantile Disease Mapping with Application to Malaria and G6PD Deficiency

Statistical analysis based on quantile regression methods is more compre...

Nonparametric Quantile Regression for Homogeneity Pursuit in Panel Data Models

Many panel data have the latent subgroup effect on individuals, and it i...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.