Modeling veterans' health benefit grants using the expectation maximization algorithm

Tatjana Miljkovic and Nikita Barabanov

Journal of Applied Statistics, 2015, vol. 42, issue 6, 1166-1182

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:42:y:2015:i:6:p:1166-1182

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DOI: 10.1080/02664763.2014.999029

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