The main features of the HPQUANTSELECT procedure are as follows:

  • Model specification

    • supports quantile regression for single or multiple quantile levels

    • supports GLM and reference cell parameterization for classification effects

    • supports any degree of interaction (crossed effects) and nested effects

    • supports statistical inferences with or without iid errors assumption

    • supports hierarchy among effects

    • supports partitioning of data into training, validation, and testing roles

    • supports a CODE statement to write SAS DATA step code to a file or catalog entry for computing predicted quantiles

    • supports a WEIGHT statement for a weighted analysis

  • Selection control

    • provides multiple effect-selection methods

    • offers selection of individual levels of classification effects

    • provides effect selection based on a variety of selection criteria

    • provides stopping rules based on a variety of model evaluation criteria

  • Display and output

    • produces output data sets that contain predicted values, residuals, standardized errors, and confidence limits of predicted values

The HPQUANTSELECT procedure supports the following effect-selection methods. For more information about these methods, see the section Methods.

forward selection

starts with no effects in the model and adds effects.

backward elimination

starts with all effects in the model and deletes effects.

stepwise selection

is similar to the forward selection method except that effects already in the model do not necessarily stay there.