Longitudinal Quantile Regression

Quantile Regression for Longitudinal Data Roger Koenker

The penalized least squares interpretation of the classical random effects estimator suggests a possible way forward for quantile regression models with a large number of fixed effects. Sparse linear algebra and interior point methods for solving large linear programs are essential practical tools.

This paper appears in 2004 in J. of Mult. Analysis, 91, 74-89.

A very basic version of the software is available here. The paper is available in pdf.

Comments are, of course, always welcome.