Modeling Multivariate Correlated Binary Data
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 4, July 2016, Pages: 225-233
Received: Jun. 13, 2016; Accepted: Jun. 22, 2016; Published: Jul. 13, 2016
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Author
Ahmed Mohamed Mohamed El-Sayed, High Institute for Specific Studies, Department of Management Information Systems, Nazlet Al-Batran, Giza, Egypt
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Abstract
This paper provides the model, estimation and test procedures for the measures of association in the correlated binary data associated with covariates in multivariate case. The generalized linear model (GLM) which satisfies the Markov properties for serial dependence, and the alternative quadratic exponential form (AQEF) are employed for multivariate Bernoulli outcome variables. The log-odds ratios as measures of association have been estimated, and the appropriate test procedures are suggested. The over-dispersion measure is investigated for the multivariate correlated binary outcomes. The scaled deviance is used as a goodness of fit of the model. For comparison, we have used the data on the respiratory disorder. In such situation, we indicate that the vectorized generalized linear models (VGLM) and AQEF procedures have the same estimates of regression parameters in the bivariate case.
Keywords
Multivariate Bernoulli Distribution, Generalized Linear Model, Scaled Deviance Test, Likelihood Ratio Test, Maximum Likelihood Estimators, Alternative Quadratic Exponential Form
To cite this article
Ahmed Mohamed Mohamed El-Sayed, Modeling Multivariate Correlated Binary Data, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 4, 2016, pp. 225-233. doi: 10.11648/j.ajtas.20160504.19
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Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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