Document Type

Article

Publication Date

2012

DOI

10.5402/2012/896082

Publication Title

ISRN Probability and Statistics

Volume

2012

Pages

896082 (10 pp.)

Abstract

While univariate instances of binomial data are readily handled with generalized linear models, cases of multivariate or repeated measure binomial data are complicated by the possibility of correlated responses. Likelihood-based estimation can be applied by using mixture distribution models, though this approach can present computational challenges. The logistic transformation can be used to bypass these concerns and allow for alternative estimating procedures. One popular alternative is the generalized estimating equation (GEE) method, though systematic errors can lead to infeasible correlation estimates or nonconvergence problems. Our approach is the coupling of quasileast squares (QLSs) method with a rarely used matrix factorization, which achieves a simplified estimation platform—as compared to the mixture model approach—and does not suffer from the convergence problems in GEE method. A noncontrived example is provided that shows the mechanical breakdown of GEE using several statistical software packages and highlights the usefulness of the QLS approach.

Comments

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Copyright © 2012 N. Rao Chaganty et al.

Original Publication Citation

N. Rao Chaganty, Roy Sabo, and Yihao Deng. (2012). Alternatives to mixture model analysis of correlated binomial data. ISRN Probability and Statistics, 2012, Article ID 896082,10. doi:10.5402/2012/896082.

Download

Share

COinS