Parsimonious Classification Via Generalized Linear Mixed Models

Kauermann G, Ormerod JT, Wand MP (2010)
JOURNAL OF CLASSIFICATION 27(1): 89-110.

Journal Article | Published | English

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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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Kauermann G, Ormerod JT, Wand MP. Parsimonious Classification Via Generalized Linear Mixed Models. JOURNAL OF CLASSIFICATION. 2010;27(1):89-110.
Kauermann, G., Ormerod, J. T., & Wand, M. P. (2010). Parsimonious Classification Via Generalized Linear Mixed Models. JOURNAL OF CLASSIFICATION, 27(1), 89-110.
Kauermann, G., Ormerod, J. T., and Wand, M. P. (2010). Parsimonious Classification Via Generalized Linear Mixed Models. JOURNAL OF CLASSIFICATION 27, 89-110.
Kauermann, G., Ormerod, J.T., & Wand, M.P., 2010. Parsimonious Classification Via Generalized Linear Mixed Models. JOURNAL OF CLASSIFICATION, 27(1), p 89-110.
G. Kauermann, J.T. Ormerod, and M.P. Wand, “Parsimonious Classification Via Generalized Linear Mixed Models”, JOURNAL OF CLASSIFICATION, vol. 27, 2010, pp. 89-110.
Kauermann, G., Ormerod, J.T., Wand, M.P.: Parsimonious Classification Via Generalized Linear Mixed Models. JOURNAL OF CLASSIFICATION. 27, 89-110 (2010).
Kauermann, Göran, Ormerod, J. T., and Wand, M. P. “Parsimonious Classification Via Generalized Linear Mixed Models”. JOURNAL OF CLASSIFICATION 27.1 (2010): 89-110.
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