CNTSELECT Procedure
PROC CNTSELECT Features
The CNTSELECT procedure estimates the parameters of a count regression model by using maximum likelihood or Bayesian techniques.
The CNTSELECT procedure supports the following models for count data:
Poisson regression
Conway-Maxwell-Poisson regression
negative binomial regression with quadratic and linear variance functions (Cameron and Trivedi 1986)
zero-inflated Poisson (ZIP) model (Lambert 1992)
zero-inflated Conway-Maxwell-Poisson (ZICMP) model
zero-inflated negative binomial (ZINB) model
fixed-effects and random-effects Poisson models for panel data
fixed-effects and random-effects negative binomial models for panel data
The following list summarizes some basic features of the CNTSELECT procedure:
has model-building syntax that uses CLASS and effect-based MODEL statements familiar from SAS/Econometrics analytic procedures
performs maximum likelihood estimation
supports multiple link functions
uses the WEIGHT statement for weighted analysis
uses the FREQ statement for grouped analysis
uses the OUTPUT statement to produce a data table that contains predicted probabilities and other observationwise statistics
uses the BAYES statement to perform Bayesian estimation for all models except Conway-Maxwell-Poisson models and panel models
uses the PRIOR statement to set prior distributions for Bayesian estimation
supports effect-selection methods