KAJIAN SIMULASI OVERDISPERSI PADA REGRESI POISSON DAN BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA BALITA GIZI BURUK

Abstract

Geographic weighted regression (GWR) is one of the regression methods for spatial data. GWR with the response variable following the poisson distribution can use the geographic weighted poisson regression (GWPR). GWPR often does not complete the assumption of dispersion. The classic approach commonly used to overcome overdispersion is related to poisson distribution, which is the approach obtained from poisson and gamma distribution which is similar to negative binomial distribution function. GWR for the response variable following the negative binomial distribution can use the geographical weighted negative binomial regression (GWNBR). The data used in this study are simulation data and real data. The results of the simulation data are the tolerance limits that are still precisely modeled with GWPR are overdispersion approaching 1 based on significant amount and average p-value.. The results of research from real data, the GWNBR is the best model for overdispersion cases in malnourished children in East Java Province in 2017 compared to the GWPR based on comparison of the values ​​of AIC.