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Likelihood Analysis of Multivariate Probit Models Using a Parameter Expanded MCEM Algorithm

Summary: [This abstract is based on the authors' abstract.] A computational framework for maximum likelihood estimation of multivariate probit regression models is proposed. The method uses the Monte Carlo expectation maximization algorithm to avoid the direct evaluation of the intractable multivariate normal orthant probabilities. This approach improves efficiency and enables a closed-form solution in the M-step. Simulation studies compare the new approach to those proposed earlier.

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  • Topics: Statistics
  • Keywords: Correlated data, Monte Carlo methods, Parameter design
  • Author: Xu, Huiping; Craig, Bruce A.
  • Journal: Technometrics