Abstract
The Bidikmisi scholarship program is an education assistance program by the government of Indonesia which aims to achieve equitable access and learning opportunities at University. Bidikmisi acceptance status having a binary type (i.e. 0 and 1) produces a structure of Bernoulli mixture model with two components. The characteristics of each component can be identified through the Bernoulli mixture regression modeling by involving the covariates of Bidikmisi scholarship grantees. The estimating parameter of Bernoulli mixture regression model was performed using Bayesian-Markov Chain Monte Carlo (MCMC) approach. One of the challenges in using Bayesian-MCMC algorithm is determining the convergence of the sampler to the posterior distribution which is typically assessed using diagnostics tools. In this paper, we present that the diagnostics tools such as Geweke method, Gelman-Rubin method, Raftery-Lewis method and Heidelberger-Welch method can give different results to conclude MCMC convergence. The improvement of convergence indicators occurs on Gelman-Rubin method and Heidelberger-Welch method when the number of iterations is increased.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Iriawan, N.: Pemodelan dan Analisis Data-Driven. ITS Press, Surabaya (2012)
Nadif, M., Govaert, G.: Clustering for binary data and mixture models-choice of the model. Appl. Stoch. Mod. Bus. Ind. 13, 269–278 (1997). https://doi.org/10.1002/(SICI)1099-0747(199709/12)13:3/4%3c269:AID-ASM321%3e3.0.CO;2-7
Grun, B., Leisch, F.: Finite mixtures of generalized linear regression models. In: Shalabh, C.H. (ed.) Recent Advances in Linear Models and Related Areas, pp. 205–230. Springer, Heidelberg (2008)
Alkan, N.: Assessing convergence diagnostic tests for bayesian cox regression. Comm. Stat. Sim. Comp. 46(4), 3201–3212 (2017). https://doi.org/10.1080/03610918.2015.1080835
Gelman, A., Rubin, D.: Inference from iterative simulation using multiple sequences. Stat. Sci. 7(4), 457–511 (1992). https://doi.org/10.1214/ss/1177011136
Geweke, J.: Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: Bernardo, J.M., Smith, A.F.M., Dawid, A.P., Berger, J.O. (eds.) Bayesian Statistics 4, pp. 169–193. Oxford University Press, New York (1992)
Raftery, A.E., Lewis, S.M.: How many iterations in the gibbs sampler? In: Bernardo, J.M., Smith, A.F.M., Dawid, A.P., Berger, J.O. (eds.) Bayesian Statistics 4, pp. 762–773. Oxford University Press, New York (1992)
Heidelberger, P., Welch, P.D.: Simulation run length control in the presence of an initial transient. Oper. Res. 31(6), 1109–1144 (1983). https://doi.org/10.1287/opre.31.6.1109
Lunn, D., Spiegelhalter, D., Thomas, A., Best, N.: The BUGS project: evolution, critique and future directions (with discussion). Stat. Med. 28(25), 3049–3082 (2009). https://doi.org/10.1002/sim.3680
Plummer, M., Best, N., Cowles, K., Vines, K.: CODA: convergence diagnosis and output analysis for MCMC. R News 6(1), 7–11 (2006)
Acknowledgements
The Authors are grateful to DRPM-Kemenristekdikti Indonesia which supported this research under PUPT research grant no. 608/PKS/ITS/2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Iriawan, N., Fithriasari, K., Ulama, B.S.S., Susanto, I., Suryaningtyas, W., Pravitasari, A.A. (2019). On the Markov Chain Monte Carlo Convergence Diagnostic of Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification. In: Kor, LK., Ahmad, AR., Idrus, Z., Mansor, K. (eds) Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017). Springer, Singapore. https://doi.org/10.1007/978-981-13-7279-7_49
Download citation
DOI: https://doi.org/10.1007/978-981-13-7279-7_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7278-0
Online ISBN: 978-981-13-7279-7
eBook Packages: Computer ScienceComputer Science (R0)