Examination of multiple conditional quantile functions provides a comprehensive view of the
relationship between the response and covariates. In situations where quantile slope …

Conventional analysis using quantile regression typically focuses on fitting the regression
model at different quantiles separately. However, in situations where the quantile …

When modelling multiple conditional quantiles of univariate and/or multivariate responses, it
is of great importance to share strength among them. The simultaneous multiple quantiles …

Simultaneously estimating multiple conditional quantiles is often regarded as a more
appropriate regression tool than the usual conditional mean regression for exploring the …

In ordinary quantile regression, quantiles of different order are estimated one at a time. An
alternative approach, which is referred to as quantile regression coefficients modeling …

Varying covariate effects often manifest meaningful heterogeneity in covariate-response
associations. In this paper, we adopt a quantile regression model that assumes linearity at a …

Quantile regression is a very useful statistical tool for estimating conditional quantile
regression functions. It has been intensively studied after its introduction by Koenker and …

The coefficients of a quantile regression model are one-to-one functions of the order of the
quantile. In standard quantile regression (QR), different quantiles are estimated one at a …

The importance of variable selection and regularization procedures in multiple regression
analysis cannot be overemphasized. These procedures are adversely affected by predictor …

Quantile regression provides a more thorough view of the effect of covariates on a response.
Non‐parametric quantile regression has become a viable alternative to avoid restrictive …