The QUANTREG Procedure

offers simplex, interior point, and smoothing algorithms for estimation

provides sparsity, rank, and resampling methods for confidence intervals

provides asymptotic and bootstrap methods for covariance and correlation matrices of the estimated parameters

provides the Wald, likelihood ratio, and rank tests for the regression parameter estimates and the Wald test for heteroscedasticity

provides outlier and leverage-point diagnostics

enables parallel computing when multiple processors are available

provides row-wise or column-wise output data sets with multiple quantiles

provides regression quantile spline fits

produces fit plots, diagnostic plots, and quantile process plots by using ODS Graphics