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QUANTSELECT Procedure


The QUANTSELECT procedure performs effect selection in the framework of quantile regression. A variety of effect selection methods are available, including greedy methods and penalty methods. PROC QUANTSELECT offers extensive capabilities for customizing the effect selection processes with a variety of candidate selecting, effect-selection stopping, and final-model choosing criteria. It also provides graphical summaries for the effect selection processes. The following are highlights of the QUANTSELECT procedure's features:

  • supports the following model specifications:
    • interaction (crossed) effects and nested effects
    • constructed effects such as regression splines
    • hierarchy among effects
    • partitioning of data into training, validation, and testing roles
  • provides the following selection controls:
    • multiple methods for effect selection
    • selection for quantile process and single quantile levels
    • selection of individual or grouped effects
    • selection based on a variety of selection criteria
    • stopping rules based on a variety of model evaluation criteria
  • provides graphical representations of the selection process
  • provides output data sets that contain predicted values and residuals
  • provides an output data set that contains the parameter estimates from a quantile process regression
  • provides an output data set that contains the design matrix
  • provides macro variables that contain selected effects

For further details see the SAS/STAT User's Guide: The QUANTSELECT Procedure
( PDF | HTML )

Examples