
Data defines the model by dint of genetic programming, producing the best decile table.


ZeroInflated Regression: Modeling a Distribution with a Mass at Zero Bruce Ratner, Ph.D. 

The standard approach for modeling a continuous target variable is the ordinary leastsquares (OLS) regression model. One of the assumptions of OLS regression: the target variable is mainly continuous with permissible discontinuities and minor clumping at several values, including the value zero. If the target variable’s distribution has a mass at zero, then OLS regression renders questionable results. The purpose of this article is to present the flexible (nonparametric, assumptionfree, datadefined model structure) GenIQ approach for modeling a continuous target variable with a mass at zero, a situation quite common in direct and database marketing, CRM, catalogue campaign management, risk assessment, and the like. I illustrate the ZeroInflated Regression GenIQ Model using a real case study, focusing in on sales per account. I use a scaleddown version of the original data to make the application tractable. But suffice it to say, GenIQ is most valuable in big data settings.
Click here for the ZeroInflated Regression illustration.

For more information about this article, call Bruce Ratner at 516.791.3544 or 1 800 DM STAT1; or email at br@dmstat1.com. 
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