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

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  • Yau, Kelvin K. W.
  • Lee, Andy H.
  • Ng, Angus S. K.

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  • Yau, Kelvin K. W. & Lee, Andy H. & Ng, Angus S. K., 2003. "Finite mixture regression model with random effects: application to neonatal hospital length of stay," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 359-366, January.
    • Handle: RePEc:eee:csdana:v:41:y:2003:i:3-4:p:359-366
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    References listed on IDEAS

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    1. Xiao, Jianguo & Lee, Andy H. & Vemuri, Siva Ram, 1999. "Mixture distribution analysis of length of hospital stay for efficient funding," Socio-Economic Planning Sciences, Elsevier, vol. 33(1), pages 39-59, March.
    2. Leung, K.-M. & Elashoff, R.M. & Rees, K.S. & Hasan, M.M. & Legorreta, A.P., 1998. "Hospital- and patient-related characteristics determining maternity length of stay: A hierarchical linear model approach," American Journal of Public Health, American Public Health Association, vol. 88(3), pages 377-381.
    3. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
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    Cited by:

    1. Chungkham Singh & Laishram Ladusingh, 2010. "Inpatient length of stay: a finite mixture modeling analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 119-126, April.
    2. Xiong, Yingge & Mannering, Fred L., 2013. "The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 39-54.
    3. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    4. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    5. Spark C. Tseung & Ian Weng Chan & Tsz Chai Fung & Andrei L. Badescu & X. Sheldon Lin, 2022. "A Posteriori Risk Classification and Ratemaking with Random Effects in the Mixture-of-Experts Model," Papers 2209.15212, arXiv.org.
    6. Charlotte Articus & Jan Pablo Burgard, 2014. "A Finite Mixture Fay Herriot-type model for estimating regional rental prices in Germany," Research Papers in Economics 2014-14, University of Trier, Department of Economics.
    7. Young, D.S. & Hunter, D.R., 2010. "Mixtures of regressions with predictor-dependent mixing proportions," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2253-2266, October.
    8. Alegre, Joaquín & Mateo, Sara & Pou, Llorenç, 2011. "A latent class approach to tourists’ length of stay," Tourism Management, Elsevier, vol. 32(3), pages 555-563.
    9. Yan Meng & Xueyan Zhao & Xibin Zhang & Jiti Gao, 2017. "A panel data analysis of hospital variations in length of stay for hip replacements: Private versus public," Monash Econometrics and Business Statistics Working Papers 20/17, Monash University, Department of Econometrics and Business Statistics.
    10. Luísa Novais & Susana Faria, 2021. "Comparison of the EM, CEM and SEM algorithms in the estimation of finite mixtures of linear mixed models: a simulation study," Computational Statistics, Springer, vol. 36(4), pages 2507-2533, December.

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