In many biometrical applications, the count data encountered often contain extra zeros relative to the Poisson distribution. Zero-inflated Poisson regression models are useful for analyzing such data, but parameter estimates may be seriously biased if the nonzero observations are over-dispersed and simultaneously correlated due to the sampling design or the data collection procedure. In this paper, a zero-inflated negative binomial mixed regression model is presented to analyze a set of pancreas disorder length of stay (LOS) data that comprised mainly same-day separations. Random effects are introduced to account for inter-hospital variations and the dependency of clustered LOS observations. Parameter estimation is achieved by maximizing an appropriate log-likelihood function using an EM algorithm. Alternative modeling strategies, namely the finite mixture of Poisson distributions and the non-parametric maximum likelihood approach, are also considered. The determination of pertinent covariates would assist hospital administrators and clinicians to manage LOS and expenditures efficiently.
Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Department of Epidemiology & Biostatistics, School of Public Health, Curtin University of Technology, Perth, WA 6845, Australia
%0 Journal Article
%1 citeulike:3111932
%A Yau, Kelvin K. W.
%A Wang, Kui
%A Lee, Andy H.
%C Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Department of Epidemiology & Biostatistics, School of Public Health, Curtin University of Technology, Perth, WA 6845, Australia
%D 2003
%I WILEY-VCH Verlag
%J Biom. J.
%K statistics
%N 4
%P 437--452
%R 10.1002/bimj.200390024
%T Zero-Inflated Negative Binomial Mixed Regression Modeling of Over-Dispersed Count Data with Extra Zeros
%U http://dx.doi.org/10.1002/bimj.200390024
%V 45
%X In many biometrical applications, the count data encountered often contain extra zeros relative to the Poisson distribution. Zero-inflated Poisson regression models are useful for analyzing such data, but parameter estimates may be seriously biased if the nonzero observations are over-dispersed and simultaneously correlated due to the sampling design or the data collection procedure. In this paper, a zero-inflated negative binomial mixed regression model is presented to analyze a set of pancreas disorder length of stay (LOS) data that comprised mainly same-day separations. Random effects are introduced to account for inter-hospital variations and the dependency of clustered LOS observations. Parameter estimation is achieved by maximizing an appropriate log-likelihood function using an EM algorithm. Alternative modeling strategies, namely the finite mixture of Poisson distributions and the non-parametric maximum likelihood approach, are also considered. The determination of pertinent covariates would assist hospital administrators and clinicians to manage LOS and expenditures efficiently.
@article{citeulike:3111932,
abstract = {{In many biometrical applications, the count data encountered often contain extra zeros relative to the Poisson distribution. Zero-inflated Poisson regression models are useful for analyzing such data, but parameter estimates may be seriously biased if the nonzero observations are over-dispersed and simultaneously correlated due to the sampling design or the data collection procedure. In this paper, a zero-inflated negative binomial mixed regression model is presented to analyze a set of pancreas disorder length of stay (LOS) data that comprised mainly same-day separations. Random effects are introduced to account for inter-hospital variations and the dependency of clustered LOS observations. Parameter estimation is achieved by maximizing an appropriate log-likelihood function using an EM algorithm. Alternative modeling strategies, namely the finite mixture of Poisson distributions and the non-parametric maximum likelihood approach, are also considered. The determination of pertinent covariates would assist hospital administrators and clinicians to manage LOS and expenditures efficiently.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Department of Epidemiology \& Biostatistics, School of Public Health, Curtin University of Technology, Perth, WA 6845, Australia},
author = {Yau, Kelvin K. W. and Wang, Kui and Lee, Andy H.},
biburl = {https://www.bibsonomy.org/bibtex/25401b724b7d0d4b3c7d97c9413555744/aho},
citeulike-article-id = {3111932},
citeulike-linkout-0 = {http://dx.doi.org/10.1002/bimj.200390024},
citeulike-linkout-1 = {http://www3.interscience.wiley.com/cgi-bin/abstract/104537388/ABSTRACT},
day = 1,
doi = {10.1002/bimj.200390024},
interhash = {b3bb5b2d030b37fb45fa5176b3943c7a},
intrahash = {5401b724b7d0d4b3c7d97c9413555744},
issn = {1521-4036},
journal = {Biom. J.},
keywords = {statistics},
month = jun,
number = 4,
pages = {437--452},
posted-at = {2014-11-26 23:02:16},
priority = {2},
publisher = {WILEY-VCH Verlag},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Zero-Inflated Negative Binomial Mixed Regression Modeling of Over-Dispersed Count Data with Extra Zeros}},
url = {http://dx.doi.org/10.1002/bimj.200390024},
volume = 45,
year = 2003
}