Parsimonious Classification Via Generalized Linear Mixed Models |
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| Authors: | G. Kauermann J. T. Ormerod M. P. Wand |
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| Affiliation: | (1) Department of Radiology, Faculty of Health Sciences, Medical School, University of Stellenbosch, Room 5019, 5th Floor, Clinical Building, Tygerberg, 7505, South Africa;(2) Department of Radiology, Royal Hospital for Sick Children, Bristol, UK |
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| Abstract: | We devise a classification algorithm based on generalized linear mixed model (GLMM) technology. The algorithm incorporates spline smoothing, additive model-type structures and model selection. For reasons of speed we employ the Laplace approximation, rather than Monte Carlo methods. Tests on real and simulated data show the algorithm to have good classification performance. Moreover, the resulting classifiers are generally interpretable and parsimonious. |
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