Title

Poverty Analysis with INLA in Hierarchical Bayesian Spatio-temporal Models

Abstract
A great opportunity on the availability of geo-reference data allows researcher to analyze not only on its spatial pattern but also including space-time interactions among regions. Spatio-temporal models designed with hierarchical fashion will be presented in this research proposal, with INLA (Integrated Nested Laplace Approximation) as the current estimation method for Bayesian analysis. INLA based on latent Gaussian posterior distribution which provides great computational benefit rather than Markov Chain Monte Carlo (MCMC). We will model the poverty data set using classical, dynamic and space-time interaction of spatio-temporal models, and investigate the poverty relationship with socio-economics information on its ecological regressions. All computational aspect will be solved using R-INLA package and deviance information criteria for models best fit selection
Keywords
Areal Data, Gaussian Markov Random Field, Integrated Nested Laplace Approximation, Markov Chain Monte Carlo, Random Walk
Source of Fund
Hibah BINUS
Funding Institution
BINUS
Fund
Rp.10.000.000,00
Contract Number
014/VR.RTT/III/2018
Author(s)
  • Drs. Iwa Sungkawa, M.S.

    Drs. Iwa Sungkawa, M.S.

  • Ro'fah Nur Rachmawati, S.Si., M.Si

    Ro'fah Nur Rachmawati, S.Si., M.Si

  • Anita Rahayu, S.Si., M.Si.

    Anita Rahayu, S.Si., M.Si.