Parsimonious Classification Via Generalized Linear Mixed Models

被引：1

作者：

Kauermann, G. ^{[1
]}

Ormerod, J. T. ^{[2
]}

Wand, M. P. ^{[3
]}

机构：

[1] Univ Bielefeld, Fac Econ, D-33501 Bielefeld, Germany

[2] Univ New S Wales, Sch Math & Stat, Sydney, NSW 2052, Australia

[3] Univ Wollongong, Sch Math & Appl Stat, Wollongong, NSW 2522, Australia

来源：

JOURNAL OF CLASSIFICATION | 2010年 / 27卷 / 01期

基金：

澳大利亚研究理事会;

关键词：

Akaike Information Criterion; Feature selection; Generalized additive models; Penalized splines; Supervised learning; Model selection; Rao statistics; Variance components; AKAIKE INFORMATION; REGRESSION; SPLINES;

D O I：

10.1007/s00357-010-9045-9

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：89 / 110

页数：22

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