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ITS » PhD Theses » S3 - Statistika
Posted by tondoindra@gmail.com at 22/09/2014 22:37:05  •  1759 Views


PENGEMBANGAN MODEL LINEAR HIRARKI DENGAN PENDEKATAN BAYESIAN UNTUK PEMODELAN DATA PENGELUARAN PERKAPITA RUMAHTANGGA

DEVELOPMENT IN HIERARCHICAL LINEAR MODEL WITH BAYESIAN APPROACH ON THE MODELING OF PER CAPITA HOUSEHOLD EXPENDITURE DATA

Author :
ISMARTINI, PUDJI  ( 1309301704 )




ABSTRAK

Disertasi ini bertujuan untuk melakukan pengembangan model linear hirarki Bayes untuk data pengeluaran perkapita rumahtangga. Pengembangan model dilakukan dengan memperhitungkan kekhususan yang ada dalam data yang dimiliki BPS yang memuat struktur hirarki khusus dengan sumber variasi yang kompleks dan pola distribusi data pengeluaran perkapita rumahtangganya memiliki karakteristik distribusi skewed kanan. Kekhususan kondisi data tersebut akan menjadi salah satu keunikan dari penelitian ini. Pemodelan tersebut dilakukan dengan menggunakan distribusi Log-normal tiga parameter LN3 dan Log-logistik tiga parameter LLD3. Proses estimasi dilakukan dengan pendekatan Bayesian menggunakan Markov Chain Monte Carlo MCMC dan algoritma Gibbs Sampling. Pemodelan dilakukan dengan model linear hirarki dua tingkat menggunakan karakteristik rumahtangga di tingkat pertama dan karakteristik wilayah kabupatenkota di tingkat kedua. Hasil yang diperoleh menunjukkan bahwa kedua alternatif model memiliki kinerja yang hampir sama dalam memprediksi nilai pengeluaran perkapita rumahtangga. Keragaman data yang dapat dijelaskan oleh kedua model tersebut adalah 547 untuk model LN3 dan 539 untuk model LLD3. Hasil diagnostik model dan validasi silang dengan data testing menunjukkan bahwa model LN3 lebih dapat diandalkan untuk memprediksi pengeluaran perkapita rumahtangga dibandingkan model LLD3. Validitas model menunjukkan model tersebut masih layak dan eliabel digunakan hingga satu tahun kedepannya yaitu tahun 2010.


ABSTRACT

The objective of this dissertation is to undertake the development of hierarchical linear models for modelling per capita household expenditure data by using the case of Central Java Province. The model development is done by taking into account the uniqueness of the data held by BPS comprising a particular hierarchical structure with a complex source of variation and also the characteristic of the distribution pattern of per capita household expenditure data that has a right-skewed distribution. Specificity of these data will be one of the uniqueness of this study. This modeling is set on the basis of the three parameters of log-normal distribution LN3 and the three parameters of log-logistic distribution LLD3. The estimation process is then accomplished by using a Bayesian approach with Markov Chain Monte Carlo MCMC and Gibbs sampling algorithms. The modeling scheme utilized in this research is the two levels of hierarchical linear model by using household characteristics at the first level and the district characteristics on the second level. The results obtained show that the two alternative models yield a quite similar performance in predicting the value of per capita household expenditure. The variation of data that can be explained by both LN3 and LLD3 models was 54.7 and 53.9 respectively. However the diagnostic and cross validation models clarify that the LN3 model is more reliable to predict per capita household expenditure than the LLD3 model. It is also certified that the LN3 model is feasible and reliable to be applied up to one year ahead ie in 2010.



KeywordsModel Linear Hiraki; Bayesian; MCMC; Gibbs Sampling; LN3; LLD3; pengeluaran perkapita rumahtangga
 
Subject:  Analisis multivarian; Statistik
Contributor
  1. Prof. Drs. Nur Iriawan, M.IKomp., Ph.D.
  2. Dr. Ir. Setiawan, MS.
Date Create: 27/08/2013
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-PhD-13004140000089
Collection ID: 13004140000089
Call Number: RDSt 519.542 Ism p


Source
PhdTheses of Statistic RDSt 519.542 Ism p,2014

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Copyright @2013 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




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  1.  ITS-PhD-31697-1309301704-Abstract_id.pdf - 177 KB
  2.  ITS-PhD-31697-1309301704-Abstract_en.pdf - 177 KB
  3.  ITS-PhD-31697-1309301704-Conclusion.pdf - 222 KB




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