Generalized Multilevel Linear Model dengan Pendekatan Bayesian untuk Pemodelan Data Pengeluaran Perkapita Rumah Tangga

Abstract

Household per capita expenditure data is one of the important information as an approach to measure the level of prosperity in an area. Such data is needed by the government, both at the central and regional levels in formulating, implementing and evaluating the implementation of development programs. This research is aimed at modeling the household per capita expenditure data which takes into account the specificity of BPS data which has a hierarchical structure, and data distribution pattern which has the right skewed characteristic. The modeling is done by using the three parameters of Log-normal distribution (LN3P) and the three parameters of Log-logistics (LL3P) with a single level (unilevel) and two levels (multilevel) structure. The parameter estimation process is done by Markov Chain Monte Carlo (MCMC) method and Gibbs Sampling algorithm. The results showed that on the unilevel model, the LL3P model is better than the LN3P model. While in multilevel model, LN3P model is better than LL3P model. The results also show that the best model for modeling household per capita expenditure data is the LN3P multilevel model with the smallest Deviance Information Criterion (DIC) value.