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.  More specifically, they find a regression model of the form g(E/Y)= b0+b1L1+...+bmLm  where both the coefficients b0,b1,...,bm and the logical expressions Lj, j=1,...,m  are determined.  The logical expressions  are logical relationships among the predictor variables, such as "X1,X2 are true but not X5"  , or "X3,X5,X7 are true but not X1 or X2".  In their paper, the authors investigate the use a simulated annealing algorithm.  In this paper, we use the Group Method of Data Handling (GMDH) to approach the problem.

 

 

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

     

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Published

2010-04-09

Issue

Vol. 3 No. 2: (April 2010)

Section

Mathematical Statistics

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How to Cite

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