The Oxford Handbook of Panel Data examines new developments in the theory and
applications of panel data. It includes basic topics like non-stationary panels, co-integration …

We propose a prediction procedure for the functional linear quantile regression model by
using partial quantile covariance techniques and develop a simple partial quantile …

We consider measures of Granger causality in quantiles, which detect and quantify both
linear and nonlinear causal effects between random variables. The measures are based on …

To study quantile regression in partial functional linear model where response is scalar and
predictors include both scalars and multiple functions, wavelet basis are usually adopted to …

The increasing availability of panel data has nourished fast-growing development in panel
data econometrics analysis. Textbooks and survey articles have been published to help …

We consider quantile regression for partially linear models where an outcome of interest is
related to covariates and a marker set (eg, gene or pathway). The covariate effects are …

In this paper, we consider the estimation and inference of the parameters and the
nonparametric part in partially linear quantile regression models with responses that are …

Functional data such as curves and surfaces have become more and more common with
modern technological advancements. The use of functional predictors remains challenging …

In this paper, we consider the confidence interval construction for partially linear quantile
regression models with missing response at random. We propose an imputation based …

Quantile regression is a powerful tool for detecting exposure-outcome associations given
covariates across different parts of the outcome's distribution, but has two major limitations …