SAS/STAT Software

QUANTREG Procedure

The QUANTREG procedure uses quantile regression to model the effects of covariates on the conditional quantiles of a response variable. The following are highlights of the QUANTREG procedure's features:

  • 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 and likelihood ratio tests for the regression parameter estimates
  • perform hypothesis tests for the estimable functions, construct confidence limits, and obtain specific nonlinear transformations
  • enables you to construct special collections of columns for design matrices
  • provides outlier and leverage-point diagnostics
  • supports parallel computing when multiple processors are available
  • provides row-wise or column-wise output data sets with multiple quantiles
  • provides regression quantile spline fits
  • automatically produces fit plots, diagnostic plots, and quantile process plots by using ODS Graphics
  • performs BY group processing, whcih enables you to obtain separate analyses on grouped observations
  • perform weighted estimation
  • creates an output data set that contains predicted values, residuals, estimated standard errors, and other statistics
  • creates an output data set that contains the parameter estimates for all quantiles
  • create a SAS data set that corresponds to any output table

For further details see the QUANTREG Procedure