# A Self-Organizing Model for Logic Regression

## Authors

• Stanley Jerry Farlow University of Maine

## Keywords:

Logic Regression, GMDH algorithm, Self-organizing methods

## Abstract

Logic regression, as developed by Ruczinski, Kooperberg, and LeBlanc (Ruczinski, Kooperberg, and LeBlanc 2003) is a multivariable regression methodology that constructs logical relationships among Boolean predictor variables that best predicts a Boolean dependent variable.Â  More specifically, they find a regression model of the form g(E/Y)= b0+b1L1+...+bmLmÂ Â where both the coefficients b0,b1,...,bmÂ and the logical expressions Lj, j=1,...,m Â are determined.Â  The logical expressionsÂ Â are logical relationships among the predictor variables, such as "X1,X2Â are true but not X5"Â Â , or "X3,X5,X7 are true but not X1Â or X2".Â  In their paper, the authors investigate the use a simulated annealing algorithm.Â  In this paper, we use the Group Method of Data Handling (GMDH) to approach the problem.

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## Author Biography

• Stanley Jerry Farlow, University of Maine

Professor of Mathematics

University of Maine

USA

I have a Ph.D in mathematics and have been a professor of mathematics at the University of Maine for 42 years.Â Â  Before that I was a Lieutenant Commander in the Public Health Service at the National Institutes of Health in Washington, D.C.Â Â  I have published papers in operations research, statistics, partial differential equations, and control theory.Â  I have also written more than 10 textboos in mathematics, some translated into Japanese, Indonesian, and Russian.

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2010-04-09

## Section

Mathematical Statistics

## How to Cite

A Self-Organizing Model for Logic Regression. (2010). European Journal of Pure and Applied Mathematics, 3(2), 163-173. https://ejpam.com/index.php/ejpam/article/view/602

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