SEMIPARAMETRIK MULTILEVEL ZERO-INFLATED GENERALIZED POISSON REGRESSION MODELING ON TRAFFIC ACCIDENT DATA IN TEMANGGUNG REGENCY

Bani Muhamad Isa; Nur Karomah Dwidayati

doi:10.15294/ujm.v9i2.34761

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Affiliations

Bani Muhamad Isa
Isa

Nur Karomah Dwidayati
UNNES

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SEMIPARAMETRIK MULTILEVEL ZERO-INFLATED GENERALIZED POISSON REGRESSION MODELING ON TRAFFIC ACCIDENT DATA IN TEMANGGUNG REGENCY

Bani Muhamad Isa ,
Nur Karomah Dwidayati

Vol 9 No 2 (2020)

DOI 10.15294/ujm.v9i2.34761

Submitted: Oct 14, 2019

Published: Dec 30, 2020

Abstract

This study aims to model the data of traffic accidents in Temanggung Regency with a multilevel zero-inflated generalized poisson semiparametric regression model. Multilevel zero-inflated generalized poisson semiparametric regression is a regression model for analyzing poisson distribution data with stratified data structures that are overdispersed and there are parametric and nonparametric components in the independent variable. This study uses the variable of many accidents as the response variable, as well as the variable of many traffic light violations, many violations of drivers not having a SIM, many accidents because the vehicle is not fit, many accidents due to damaged roads as the independent variable. The method used to estimate the model parameters is the Maximum Likelihood Ratio (MLE) method with the Maximization Expectation (EM) algorithm. After estimating the parameters and the suitability of the test model with the Wald Test, then the model shape is obtained a semiparametric regression multilevel zero inflated generaized poison with AIC count model 144.0032 and AIC zero-inflation model -63.0016.