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ITS » Research Report » Statistika
Posted by dwi at 16/01/2008 17:56:15  •  14969 Views


Author :
Nur Iriawan 




ABSTRAK

Identifikasi data dengan menganalisis dan menguji keluarga distribusi datanya biasa dilakukan dengan menggunakan alat uji goodness of fit test. Tetapi pada data dengan adanya gejala multi modal akan cukup sulit untuk dapat dilakukan dengan alat uji tersebut karena memang alat uji ini dapat dipakai apabila telah diketahui distribusi dugaannya. Sementara pendugaan distribusi untuk multi-modal cukup rumit untuk dilakukan. Penelitian ini mencobakan mixture normal untuk menjembatani kesulitan identifikasi distribusi di atas. Selanjutnya hasil identifikasi ini dipadukan dengan berbagai nilai yang memungkinkan sehingga dapat dibentuk model mixture regresi. Pengembangan studi mixture ini diterapkan dengan menggunakan MCMC Markov Chain Monte Carlo dan digunakan dua jenis data yaitu data simulasi dan data IHK Indeks Harga Konsumen. Akhir pemodelannya dapat diperoleh hasil bahwa model mixture dapat memberikan estimasi distribusi yang lebih baik untuk multi-modal data dan dapat merepresentasikan data regresi lebih informatif dari pada cara biasa. Kata kunci mixture normal mixture regresi linear Bayesian Markov Chain Monte Carlo Gibbs sampling marginal posterior


ABSTRACT

Goodness of fit method is frequently used to analyze and identify of which distribution the data come from. In the context of known uni-modal distribution this method shows its powerful tool. When the data follows the unknown multi-modal form however it will fail to identify the family of distribution of the data. On the other hand the identification of the multi-modal distribution itself is not easy to be done. This research describes mixture model to identify the multi-modal data by implementing Bayesian method of estimation in couple with Markov Chain Monte Carlo scheme especially Gibbs sampler. To show the work of the combination of these methods in presenting the idea this research use simulated data and real data from BPS which are modeled as a mixture distribution and mixture of linear regression. The end of the research shows that mixture model gives a significant improvement in identifying the multi-modal data. Key words mixture normal mixture linear regression Bayesian Markov Chain Monte Carlo Gibbs sampling marginal posterior.



KeywordsMixture normal ; mixture regresi linear ; Bayesian ; Markov Chain Monte Carlo ; Gibbs sampling ; marginal posterior
 
Subject:  Probabilitas
Date Create: 16/01/2008
Type: Text
Format: pdf ; 73 pages
Language: Indonesian
Identifier: ITS-Research-3100005066029
Collection ID: 3100005066029
Call Number: ITS 519.233 Iri s


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