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dc.contributor.authorNitivijaya, Maulidiah
dc.contributor.authorIriawan, Nur
dc.contributor.authorKuswanto, Heri
dc.date.accessioned2015-12-05T07:50:01Z
dc.date.available2015-12-05T07:50:01Z
dc.date.issued2015-12-07
dc.identifier.citation[1] G. Brunello and D. Checchib. School quality and family background in Italy. Economics of Education Review, 24:563–577, 2005. [2] B. Santoso. Spline Multivariable and MARS Approach on Modeling Years of Schooling on The School Age Population in Papua province. Thesis, Institut Teknologi Sepuluh Nopember Surabaya, 2009. [3] J. Qudsi. Mixture Survival Spasial Models for Years of Schooling on School Age 16-18 years in East Java 2012. Thesis, Institut Teknologi Sepuluh Nopember, Surabaya, 2015. [4] G. McLachlan and K. E. Basford. Mixture Models Inference and Applications to Clustering. Marcel Dekker, 1988. [5] N. Iriawan and S. P. Astuti. Mengolah Data Statistik dengan Mudah Menggunakan Minitab 14. Andi Offset, Yogyakarta. 2006. [6] D. G. Kleinbaum and M. Klein. Survival Analysis: A Self Learning, 2nd Edition. Springer, 2005. [7] E. Lee. Statistical Models and Methods for Lifetime Data. John Wiley and Sons Inc., 1992. [8] Zang. Survival Analysis. Wadsworth, 2008. [9] D. W. Hosmer Jr. and S. Lemeshow. Applied Survival Analysis: Regression Modelling of Time to Event Data. John Wiley and Sons Inc., 1999. [10] J. M. Marin, K. L. Mengersen, and C.P. Robert. Bayesian Modelling and Inference on Mixtures of Distributions, Handbook of Statistics, volume 25, Elsevier, 2005. [11] M. Stephen. Bayesian Metods for Mixture of Normal Distribution. Thesis, University of Oxford, UK, 1997. [12] D. Gamerman. Markov Chain Monte Carlo. Chapman & Hall, 1997. [13] N. Iriawan. Computationally Intensive Approaches to Inference in Neo-Normal Linier Models, Thesis Ph.D., CUT-Australia. 2000. [14] G. E. P. Box and G. C. Tiao. Bayesian Inference in Statistical Analysis. Addison-Wesley, 1973. [15] I. Ntzoufras. Bayesian Modelling Using WinBUGS. John Willey and Sons Inc., 2009. [16] B. P. Carlin and S. Chib. Bayesian model choice via Markov Chain Monte Carlo methods. Journal of The Royal Statistical Society, Vol. 57(3): page 473-484, 1995. [17] G. Casella and E. I. George. Explaining Gibbs Sampler. The American Statistician, Vol. 46(3): page 167-174, 1992.in_ID
dc.identifier.issn2477-3328
dc.identifier.urihttp://hdl.handle.net/11617/6316
dc.description.abstractEducation could be considered as one of the basic pillars to determine the performance indicator of a respective region. Year of schooling is one of the education indexes, which becomes the government's target in the 9-year compulsory education program. This index illustrates the importance of knowledge and higher-level skills. Meanwhile, West Papua Province as one of the youngest provinces in Indonesia is challenged to improve the quality of human resources, particularly in the underdeveloped regions. Therefore, it is important to identify the variables which influence the years of schooling in the West Papua province. Statistically, the type of data such as length of time is frequently used to be the survival analysis. Nevertheless, the distribution pattern of the response variables is difficult to be analyzed. For that reason, this study applied mixture model on years of schooling. Mixture model estimation leads to the complex statistical problems with a number of parameters. Bayesian methods accomplish the estimation through the simulation process of Markov Chain Monte Carlo (MCMC). The survival mixture model was formed based on the status of county. Rural areas were evidenced to give the contribution of years of schooling distribution more than urban area up to 59.87 percent. The opportunity to obtain formal education at least to junior high school in urban areas was greater than rural area had, yet it went down faster in year 12-th or in senior high school level. In general, the factors which influenced the years of schooling in urban and rural areas turned out to be different.in_ID
dc.language.isoenin_ID
dc.publisherUniversitas Muhammadiyah Surakartain_ID
dc.subjectmixture survivalin_ID
dc.subjectMCMCin_ID
dc.subjectCox regressionin_ID
dc.subjectyears of schoolingin_ID
dc.titleBayesian Survival Mixture Model on Years of Schooling in West Papua Provincein_ID
dc.typeArticlein_ID


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