Finite mixture regression model with random effects: application to neonatal hospital length of stay

Yau, KKW, Lee, AH and Ng, ASK (2003) Finite mixture regression model with random effects: application to neonatal hospital length of stay. Computational Statistics & Data Analysis, 41 3-4: 359-366. doi:10.1016/S0167-9473(02)00180-9


Author Yau, KKW
Lee, AH
Ng, ASK
Title Finite mixture regression model with random effects: application to neonatal hospital length of stay
Journal name Computational Statistics & Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2003
Sub-type Article (original research)
DOI 10.1016/S0167-9473(02)00180-9
Volume 41
Issue 3-4
Start page 359
End page 366
Total pages 8
Editor S. Azen
E. Kontoghiorghes
J. Lee
Place of publication Amsterdam, Netherlands
Publisher Elsevier Science BV
Collection year 2003
Language eng
Subject C1
230204 Applied Statistics
730199 Clinical health not specific to particular organs, diseases and conditions
Abstract A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.
Keyword Computer Science, Interdisciplinary Applications
Mathematics, Applied
Statistics & Probability
Em Algorithm
Generalised Linear Mixed Models
Heterogeneity
Mixture Distributions
Random Effects
Mixed Models
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2004 Higher Education Research Data Collection
School of Physical Sciences Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 25 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 30 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 15 Aug 2007, 02:07:26 EST