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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Huiping Xu1, Bruce A Craig

  • 1Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762 ( hxu@math.msstate.edu ).

Technometrics : a Journal of Statistics for the Physical, Chemical, and Engineering Sciences
|November 3, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient computational framework for multivariate probit regression models using the Monte Carlo EM algorithm. The proposed method enhances parameter estimation for complex binary data analysis.

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Area of Science:

  • Statistics
  • Computational Statistics
  • Econometrics

Background:

  • Multivariate binary data are common in various fields.
  • Maximum likelihood estimation for multivariate probit models is computationally challenging due to intractable integrals.

Purpose of the Study:

  • To develop a practical and efficient computational framework for maximum likelihood estimation of multivariate probit regression models.
  • To improve upon existing methods for handling complex multivariate binary data.

Main Methods:

  • The study employs the Monte Carlo Expectation-Maximization (MCEM) algorithm.
  • Parameter expansion is utilized in the M-step to circumvent direct evaluation of multivariate normal orthant probabilities.
  • This approach leads to a closed-form solution and enhanced computational efficiency.

Main Results:

  • The proposed MCEM algorithm with parameter expansion demonstrates competitive or superior performance compared to existing methods.
  • Simulation studies validate the efficiency and accuracy of the new framework.
  • The method is successfully applied to a real-world dataset, showcasing its practical utility.

Conclusions:

  • The proposed computational framework offers an efficient and practical solution for maximum likelihood estimation in multivariate probit models.
  • This advancement facilitates more robust analysis of complex multivariate binary data.
  • The method provides a valuable tool for researchers across various disciplines dealing with such data.