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Modeling veterans' health benefit grants using the expectation maximization algorithm

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  • Tatjana Miljkovic
  • Nikita Barabanov

Abstract

A novel application of the expectation maximization (EM) algorithm is proposed for modeling right-censored multiple regression. Parameter estimates, variability assessment, and model selection are summarized in a multiple regression settings assuming a normal model. The performance of this method is assessed through a simulation study. New formulas for measuring model utility and diagnostics are derived based on the EM algorithm. They include reconstructed coefficient of determination and influence diagnostics based on a one-step deletion method. A real data set, provided by North Dakota Department of Veterans Affairs, is modeled using the proposed methodology. Empirical findings should be of benefit to government policy-makers.

Suggested Citation

  • Tatjana Miljkovic & Nikita Barabanov, 2015. "Modeling veterans' health benefit grants using the expectation maximization algorithm," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1166-1182, June.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1166-1182
    DOI: 10.1080/02664763.2014.999029
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    3. McDonald, John F & Moffitt, Robert A, 1980. "The Uses of Tobit Analysis," The Review of Economics and Statistics, MIT Press, vol. 62(2), pages 318-321, May.
    4. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
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    Cited by:

    1. Rita Yi Man Li & Herru Ching Yu Li, 2018. "Have Housing Prices Gone with the Smelly Wind? Big Data Analysis on Landfill in Hong Kong," Sustainability, MDPI, vol. 10(2), pages 1-19, January.

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