Modeling veterans' health benefit grants using the expectation maximization algorithm

摘要

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.