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


CHAID: A Method for Filling In Missing Values
Bruce Ratner, Ph.D.

The problem of analyzing data with missing values is well known to data analysts. Data analysts know that almost all standard statistical analyses require complete data for reliable results. These analyses performed with incomplete data assuredly produce biased results. Thus, data analysts make every effort to fill in the missing data values in their datasets. The popular solutions to the problem of handling missing data belong to the collection of imputation, or fill-in techniques. This article presents CHAID as an alternative method for filling in missing data.

For more information about this article, call me at 516.791.3544, or e-mail, br@dmstat1.com.
My publisher owns the copyright of the article, about which this abstract addresses. The article will appear in my forthcoming book.
My publisher has granted me permission to discuss orally the article's content, but by no means provide an outline, draft or proof-ready of the article.


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