Document Type
Journal Article
RIS ID
33747
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.
Grant Number
ARC/DP0877055, ARC/DP0556518
Publication Details
Kauermann, G., Ormerod, J. & Wand, M. (2010). Parsimonious classification via generalized linear mixed models. Journal of Classification, 27 (1), 89-110.