The paper explores and establishes a unique Bayesian framework for estimating three shape parameters of the McDonald generalized beta-binomial distribution. The mixture distribution is used in modelling overdispersed binomial data. Foundations of the framework have been enriched by knowledge of Bayesian statistics and Markov Chain Monte Carlo methods. A Metropolis within Gibbs Monte Carlo method to sample from the unknown posterior form of the distribution was used. The shape parameters (α, β and γ) were assigned flat gamma priors to ensure equal probabilities for all the values. McDonald generalized beta-binomial variables were simulated with fixed shape parameters set at (α,β,γ)=(0.5,0.5,0.5) respectively and samples generated were used to estimate the parameters, to evaluate if the method recovers estimates close to the true parameter values. Standard errors were also computed for the simulated data and real data. Further, credible regions and highest probability density intervals (HPD) were computed and their corresponding lengths. To evaluate the marginal posterior samples for every shape parameter generated trace plots presented, their respective correlation plots were also presented and the histograms to show the distributions assumed by every parameter. Bayesian framework provides a direct and flexible method of computation for a mixture distribution whose complexity may pose challenges of integration when using the classical methods of estimation.
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Murithi, I. K. , Okenye, J. O. , Islam, A. S. and Wanyonyi, R. W. (2020). Bayesian Estimation of the Shape Parameters of Mcdonald Generalized Beta-Binomial Distribution. Open Access Library Journal, 7, e6651. doi: http://dx.doi.org/10.4236/oalib.1106651.
Stoffel, M.A., Nakagawa, S. and Schielzeth, H. (2017) Repeatability Estimation and Variance Decomposition by Generalized Linear Mixed-Effects Models. Methods in Ecology and Evolution, 8, 1639-1644. https://doi.org/10.1111/2041-210X.12797
Coveney, P., Dougherty, E. and Highfield, R. (2016) Big Data Need Big Theory Too. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 374, 1-11. http://www.jstor.org/stable/26115982
https://doi.org/10.1098/rsta.2016.0153
Richterich, A. (2018) Big Data: Ethical Debates. In: The Big Data Agenda: Data Ethics and Critical Data Studies, University of Westminster Press, London, 33-52.
https://doi.org/10.2307/j.ctv5vddsw
Petzschner, F.H., Glasauer, S. and Stephan, K.E. (2015) A Bayesian Perspective on Magnitude Estimation. Trends in Cognitive Sciences, 19, 285-293.
https://doi.org/10.1016/j.tics.2015.03.002
Manoj, C., Wijekoon, P. and Yapa, R.D. (2013) The McDonald Generalized Beta-Binomial Distribution: A New Binomial Mixture Distribution and Simulation Based Comparison with Its Nested Distributions in Handling over Dispersion. International Journal of Statistics and Probability, 2, 24.
https://doi.org/10.5539/ijsp.v2n2p24
Janiffer, N., Islam, A. and Luke, O. (2014) Estimating Equations for Estimation of McDonald Generalized Beta-Binomial Parameters. Open Journal of Statistics, 4, 702-709. https://doi.org/10.4236/ojs.2014.49065
Dodwell, T.J., Ketelsen, C., Scheichl, R. and Teckentrup, A.L. (2015) A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow. SIAM/ASA Journal on Uncertainty Quantification, 3, 1075-1108. https://doi.org/10.1137/130915005
Sprenger, J. (2018) The Objectivity of Subjective Bayesianism. European Journal for Philosophy of Science, 8, 539-558. https://doi.org/10.1007/s13194-018-0200-1
Lynch, S.M. (2007) Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. Springer Science & Business Media, Berlin.
https://doi.org/10.1007/978-0-387-71265-9
Li, J., Nott, D.J., Fan, Y. and Sisson, S.A. (2017) Extending Approximate Bayesian Computation Methods to High Dimensions via a Gaussian Copula Model. Computational Statistics & Data Analysis, 106, 77-89.
https://doi.org/10.1016/j.csda.2016.07.005
Alquier, P., Friel, N., Everitt, R. and Boland, A. (2016) Noisy Monte Carlo: Convergence of Markov Chains with Approximate Transition Kernels. Statistics and Computing, 26, 29-47. https://doi.org/10.1007/s11222-014-9521-x
Le, H., Pham, U., Nguyen, P. and Pham, T.B. (2018) Improvement on Monte Carlo Estimation of HPD Intervals. Communications in Statistics-Simulation and Computation. https://doi.org/10.1080/03610918.2018.1513141
Grzenda, W. (2015) The Advantages of Bayesian Methods over Classical Methods in the Context of Credible Intervals. Information Systems in Management, 4, 53-63.