Parsimonious classification via generalised linear mixed models

Publisher:
Springer New York LLC
Publication Type:
Journal Article
Citation:
Journal of Classification, 2010, 27 (1), pp. 89 - 110
Issue Date:
2010-01
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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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