The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of
regression coefficients. In addition, due to the quantile check loss function, it is robust …

Quantile regression has become a valuable tool to analyze heterogeneous covaraite-
response associations that are often encountered in practice. The development of quantile …

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 …

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

Quantile regression provides a flexible platform for evaluating covariate effects on different
segments of the conditional distribution of response. As the effects of covariates may change …

The quantile regression model is widely used in variable relationship research of moderate
sized data, due to its strong robustness and more comprehensive description of response …

Compared with the conditional mean regression-based scalar-on-function regression
model, the scalar-on-function quantile regression is robust to outliers in the response …

Quantile regression (QR) is an ideal alternative for depicting the conditional quantile
functions of a response variable when the conditions of linear regression are unavailable …

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 …

Quantile regression has received a great deal of attention as an important tool for modeling
statistical quantities of interest other than the conditional mean. Varying coefficient models …