Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

HGLMs for quality improvement

HGLMs for quality improvement A modelling approach has been useful for the analysis of data from robust designs for quality improvement. Recently, Robinson et al. (J. Qual. Technol. 2006; 38:65–38) proposed the use of generalized linear mixed models (GLMMs) and they used the marginal quasi‐likelihood (MQL) method of Breslow and Clayton (J. Am. Statist. Ass. 1983; 88:9–25). Hierarchical generalized linear models (HGLMs) extend GLMMs by allowing structured dispersions and conjugate distributions of arbitrary GLM families for random effects. In this paper we use two examples to illustrate how these additional features in HGLMs can be used for the analysis of data from quality‐improvement experiments. We also show that the hierarchical likelihood (HL, or h‐likelihood) estimators have better statistical properties than the MQL estimators. Copyright © 2010 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Loading next page...
 
/lp/wiley/hglms-for-quality-improvement-eWdDKE83OB

References (29)

Publisher
Wiley
Copyright
Copyright © 2011 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.840
Publisher site
See Article on Publisher Site

Abstract

A modelling approach has been useful for the analysis of data from robust designs for quality improvement. Recently, Robinson et al. (J. Qual. Technol. 2006; 38:65–38) proposed the use of generalized linear mixed models (GLMMs) and they used the marginal quasi‐likelihood (MQL) method of Breslow and Clayton (J. Am. Statist. Ass. 1983; 88:9–25). Hierarchical generalized linear models (HGLMs) extend GLMMs by allowing structured dispersions and conjugate distributions of arbitrary GLM families for random effects. In this paper we use two examples to illustrate how these additional features in HGLMs can be used for the analysis of data from quality‐improvement experiments. We also show that the hierarchical likelihood (HL, or h‐likelihood) estimators have better statistical properties than the MQL estimators. Copyright © 2010 John Wiley & Sons, Ltd.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: May 1, 2011

There are no references for this article.