Reduced-rank vector generalized linear models

被引：99

作者：

Yee, TW

Hastle, TJ

机构：

[1] Univ Auckland, Dept Stat, Auckland, New Zealand

[2] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117548, Singapore

[3] Stanford Univ, Dept Stat, Stanford, CA 94305 USA

来源：

STATISTICAL MODELLING | 2003年 / 3卷 / 01期

关键词：

categorical data analysis; iteratively reweighted least squares; linear predictors; multinomial logit model; reduced rank regression; stereotype model; vector generalized linear models; CANONICAL CORRELATION; MAXIMUM-LIKELIHOOD; REGRESSION-MODELS; LEAST-SQUARES; TIME-SERIES;

D O I：

10.1191/1471082X03st045oa

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Reduced-rank regression is a method with great potential for dimension reduction but has found few applications in applied statistics. To address this, reduced-rank regression is proposed for the class of vector generalized linear models (VGLMs), which is very large. The resulting class, which we call reduced-rank VGLMs (RR-VGLMs), enables the benefits of reduced-rank regression to be conveyed to a wide range of data types, including categorical data. RR-VGLMs are illustrated by focussing on models for categorical data, and especially the multinomial logit model. General algorithmic details are provided and software written by the first author is described. The reduced-rank multinomial logit model is illustrated with real data in two contexts: a regression analysis of workforce data and a classification problem.

引用

页码：15 / 41

页数：27

共 38 条

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