TY - JOUR
T1 - On the Markov Chain Monte Carlo Convergence Diagnostic of Bayesian Finite Mixture Model for Income Distribution
AU - Susanto, I.
AU - Iriawan, N.
AU - Kuswanto, H.
AU - Suhartono,
AU - Fithriasari, K.
AU - Ulama, B. S.S.
AU - Suryaningtyas, W.
AU - Pravitasari, A. A.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/9/28
Y1 - 2018/9/28
N2 - The assessment and comparison of income inequality and poverty can be supported by estimating the probability distribution of income. Income distributions which are typically heavy-tailed and positively skewed have been estimated both parametric and nonparametric approach. In parametric approach, finite mixtures distributions have been usefully implemented in the modelling of income distributions which has the multimodal characteristic. The Markov Chain Monte Carlo (MCMC) approach is one of the estimation methods which has a good performance in estimating the parameter of Bayesian finite mixture model. The convergence of the MCMC sampler to the posterior distribution is typically assessed using standard diagnostics methods, i.e., Gelman-Rubin method, Geweke method, Raftery-Lewis method and Heidelberger-Welch method. Those methods can give different results to conclude MCMC convergence condition. In this paper, a real sample income data from the Indonesian Family Life Survey (IFLS) 2015 and BidikMisi 2015 are employed to demonstrate the performance of diagnostics tools that assess convergence of the MCMC algorithm in estimating the parameter of Bayesian finite mixture models.
AB - The assessment and comparison of income inequality and poverty can be supported by estimating the probability distribution of income. Income distributions which are typically heavy-tailed and positively skewed have been estimated both parametric and nonparametric approach. In parametric approach, finite mixtures distributions have been usefully implemented in the modelling of income distributions which has the multimodal characteristic. The Markov Chain Monte Carlo (MCMC) approach is one of the estimation methods which has a good performance in estimating the parameter of Bayesian finite mixture model. The convergence of the MCMC sampler to the posterior distribution is typically assessed using standard diagnostics methods, i.e., Gelman-Rubin method, Geweke method, Raftery-Lewis method and Heidelberger-Welch method. Those methods can give different results to conclude MCMC convergence condition. In this paper, a real sample income data from the Indonesian Family Life Survey (IFLS) 2015 and BidikMisi 2015 are employed to demonstrate the performance of diagnostics tools that assess convergence of the MCMC algorithm in estimating the parameter of Bayesian finite mixture models.
UR - http://www.scopus.com/inward/record.url?scp=85054488552&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1090/1/012014
DO - 10.1088/1742-6596/1090/1/012014
M3 - Conference article
AN - SCOPUS:85054488552
SN - 1742-6588
VL - 1090
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012014
T2 - International Conference on Computation in Science and Engineering, ICCSE 2017
Y2 - 10 July 2017 through 12 July 2017
ER -