FROM http://support.sas.com/rnd/app/papers/glmselect.pdf
The GLMSELECT procedure performs effect selection in the framework of general linear models. A variety of
model selection methods are available, including the LASSO method of Tibshirani (1996) and the related LAR method of Efron et al. (2004). The procedure offers extensive capabilities for customizing the selection
with a wide variety of selection and stopping criteria, from traditional and computationally efficient significance-level-based criteria to more computationally intensive validation-based criteria. The procedure also provides graphical summaries of the selection
search.
The GLMSELECT procedure compares most closely to REG and GLM. The REG procedure supports a variety of model-selection methods but does
not support a CLASS statement. The GLM procedure supports a CLASS statement but does
not include effect selection methods. The GLMSELECT procedure fills this gap. GLMSELECT
focuses on the standard independently and identically distributed general linear model for univariate responses and offers great flexibility for and insight into the model selection algorithm. GLMSELECT provides results (displayed tables, output
data sets, and macro variables) that make it easy to take the selected model and explore it in more detail in a subsequent procedure such as REG or GLM.
The main features of the GLMSELECT procedure are as follows：
Model Specification
supports different parameterizations for classification effects
supports any degree of interaction (crossed effects) and nested effects
supports hierarchy among effects
supports partitioning of data into training, validation, and testing roles
supports constructed effects including spline and multimember effects
Selection Control
provides multiple effect selection methods
enables selection from a very large number of effects (tens of thousands)
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
provides leave-one-out and -fold cross validation
Display and Output
produces graphical representation of selection process
produces output data sets containing predicted values and residuals
produces an output data set containing the design matrix
produces macro variables containing selected models
supports parallel processing of BY groups
supports multiple SCORE statements