HPGENSELECT Procedure
The HPGENSELECT procedure is a highperformance procedure that provides model fitting and model building for generalized linear models.
The following are highlights of the HPGENSELECT procedure's features:
 fits models for standard distributions in the exponential family
 fits multinomial models for ordinal and nominal responses
 fits zeroinflated Poisson and negative binomial models for count data
 supports the following link functions:
 complementary loglog
 generalized logit
 identity
 reciprocal
 reciprocal squared
 logarithm
 logit
 loglog
 probit
 provides forward, backward, and stepwise variable selection

 supports the following selection criteria:
 AIC (Akaike's information criterion)
 AICC (smallsample bias corrected version of Akaike's information criterion)
 BIC (Schwarz Bayesian criterion)
 writes SAS DATA step code for computing predicted values of the fitted model either to a file or to a catalog entry
 creates a data set that contains observationwise statistics that are computed after the model is fitted
 enables you to specify how observations in the input data set are to be logically partitioned into disjoint subsets for model training, validation, and testing
 performs weighted estimation
 runs in either singlemachine mode or distributed mode

For further details see the HPGENSELECT Procedure
Examples