On the Markov Chain Monte Carlo Convergence Diagnostic of Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification-Reference-Cited by-同舟云学术

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On the Markov Chain Monte Carlo Convergence Diagnostic of Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification

Published:2019 Issue: Volume: Page:397-403
ISSN:
Container-title:Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017)
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Author:

Iriawan Nur,Fithriasari Kartika,Ulama Brodjol Sutijo Suprih,Susanto Irwan,Suryaningtyas Wahyuni,Pravitasari Anindya Apriliyanti

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-7279-7_49

Reference10 articles.

1. Iriawan, N.: Pemodelan dan Analisis Data-Driven. ITS Press, Surabaya (2012)

2. 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

3. 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)

4. 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

5. 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

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