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SEGMETASI BAYESIAN HIRARKI UNTUK MODEL AR STASIONER KONSTAN PER SEGMEN MENGGUNAKAN ALGORITMA REVERSIBLE JUMP MCMC

Dr., Suparman (2013) SEGMETASI BAYESIAN HIRARKI UNTUK MODEL AR STASIONER KONSTAN PER SEGMEN MENGGUNAKAN ALGORITMA REVERSIBLE JUMP MCMC. In: Sesindo, Desember 2013, Bali.

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Abstract

This paper addresses the problem of the data segmentation within a Bayesian framework by using reversible jump MCMC sampling. The data is modeled by piecewise constant Autoregressive (AR) processes where the numbers of segments, the time of abrupt, the order and the coefficients of the AR processes for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow to compute some interesting features of the a posterior distribution. The performance of the this methodology is illustrated via several simulation results.The results of simulation show that the reversible jump MCMC algorithm can estimate the parameters of piecewise constant autoregressive well.

Item Type: Conference or Workshop Item (Paper)
Subjects: H Social Sciences > HA Statistics
Depositing User: Dr. Suparman M, Si., DEA
Date Deposited: 01 Oct 2015 00:24
Last Modified: 01 Oct 2015 00:24
URI: http://eprints.uad.ac.id/id/eprint/2431

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