The General Monotone Model (GeMM) is a statistical algorithm for predicting rank orders from a set of k predictors. As shown in Dougherty and Thomas (2012, Psychological Review), GeMM is unaffected by any monotonic transformation of the criterion variable, unaffected by non-linear monotone relationships, and shows better power and predictive accuracy than Least-Squares regresison procedures in a variety of contexts. Whereas Least-Squares procedures can yield invalid conclusions when applied to messy data that includes a small number of outliers, our work shows that GeMM is robust to outliers.

• Article comparing GeMM to various Least Squares alternatives on real and simulated data
• Article showing GeMM's robustness to outliers (forthcoming)
• Article describing Order Constrained Linear Optimization (forthcoming)

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Contact

Michael R. Dougherty, PhD
Associate Professor of Psychology
Email: mdougher@.umd.edu
Office: Biology-Psychology Building, 1145C
Phone: (301) 405-8423