Experimental QUANTREG Procedure
New to SAS/STAT software is an experimental procedure that performs quantile regression. The QUANTREG procedure is available, by download only, for the Windows platform and works with the SAS 9.1 release.
Ordinary least squares regression models the relationship between one or more covariates X and the conditional mean of the response variable Y given X=x. Quantile regression extends the regression model to conditional quantiles of the response variable, such as the 90th percentile. Quantile regression is particularly useful when the rate of change in the conditional quantile, expressed by the regression coefficients, depends on the quantile. The main advantage of quantile regression over least squares regression is its flexibility for modeling data with heterogeneous conditional distributions. Data of this type occur in many fields, including biomedicine, econometrics, and ecology.
Some PROC QUANTREG features are:
- Implements the simplex, interior point, and smoothing algorithms for estimation
- Provides three methods to compute confidence intervals for the regression quantile parameter: sparsity, rank, and resampling.
- Provides two methods to compute the covariance and correlation matrices of the estimated parameters: an asymptotic method and a bootstrap method
- Provides two tests for the regression parameter estimates: the Wald test and a likelihood ratio test
- Uses robust multivariate location and scale estimates for leverage point detection
- Multithreaded for parallel computing when multiple processors are available