Back to Search
Start Over
Reduced-rank vector generalized linear models.
- Source :
-
Statistical Modelling: An International Journal . 2003, Vol. 3 Issue 1, p15-41. 27p. - Publication Year :
- 2003
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CATEGORIES (Mathematics)
*REGRESSION analysis
*LINEAR statistical models
Subjects
Details
- Language :
- English
- ISSN :
- 1471082X
- Volume :
- 3
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Statistical Modelling: An International Journal
- Publication Type :
- Academic Journal
- Accession number :
- 9491971
- Full Text :
- https://doi.org/10.1191/1471082X03st045oa