We provide an overview of linear quantile regression models for continuous responses
repeatedly measured over time. We distinguish between marginal approaches, that explicitly …

In this article I develop a wild bootstrap procedure for cluster-robust inference in linear
quantile regression models. I show that the bootstrap leads to asymptotically valid inference …

This paper proposes a linear quantile regression analysis method for longitudinal data that
combines the between-and within-subject estimating functions, which incorporates the …

We develop two empirical likelihood-based inference procedures for longitudinal data under
the framework of quantile regression. The proposed methods avoid estimating the unknown …

This paper considers estimation and inference for heterogeneous counterfactual effects with
high‐dimensional data. We propose a novel robust score for debiased estimation of the …

Despite the widespread popularity of linear models for correlated outcomes (eg, linear
mixed models and time series models), distribution diagnostic methodology remains …

Regression quantile estimators solve a linear program and can be computed efficiently. The
finite-sample distributions of regression quantiles can be characterized (Koenker, 2005), but …

In this paper, we propose a penalized weighted quantile estimating equations (PWQEEs)
method to obtain sparse, robust and efficient estimators for the quantile regression with …

Recently, Heras et al.(2018. An application of two-stage quantile regression to insurance
ratemaking. Scandinavian Actuarial Journal 9, 753–769) propose a two-step inference to …

Quantile regression is a powerful statistical methodology that complements the classical
linear regression by examining how covariates influence the location, scale, and shape of …