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

  • Bani Muhamad Isa Isa
  • Nur Karomah Dwidayati UNNES
Keywords: overdispersi, semiparametrik, zero-inflated, multilevel, regresi poisson, zero-inflated generalized poisson, kecelakaan

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.

References

Baillo, A, dkk. (2009). Test for Zero-Inflation and overdispersion a New Approach Based on The Stochastic Convex Order. Journal of Computational Statistics & Data Analysis, 1-27.

Dewi, N. C. S., & Budiantara, I. N. (2018). Faktor-Faktor yang Mempengaruhi Angka Kecelakaan Lalu Lintas di Provinsi Jawa Timur Menggunakan Regresi Nonparametrik Spline Truncate. Jurnal Sains dan Seni ITS Vol. 7 No.2, 1-8.

Dey, D. K., dkk, ed. (2000). Generalized Linear Models a Bayessian Perspective. New York: Marcel Dekker.

Eshraghian, M.R., dkk. (2016). Multilevel Zero-Inflated Generalized Poisson Regression Modelling for Dispersed Correlated Count Data. Journal of Statistical Methodology Vol 30, 1-14.

Famoye, F., & Singh, K. P. (2006). Zero Inflated Generalized Poisson Regression Model with an Aplication to Domestic Violence Data. Journal Teknologi, Vol, 43. No 1, 39-50.

Fathurahman, K. (2010). Pemilihan Model Terbaik Menggunakan Akaike’s Information Criterion. Jurnal Eksponensial,Vol. 1, No. 2.

Fitriani, A. dkk. (2015). Estimasi Model Regresi Semiparametrik Menggunakan Estimator Kernel Uniform. E-Jurnal Matematika, Vol. 4, No. 4, 176-180.

Fitriani, Ni Luh Ayu., dkk. (2016). Analisis Regresi Multilevel dalam Menentukan Variabel Determinan Nilai Ujian Akhir Nasional Siswa. Jurnal Matematika Vol 6, 15-21.

Fuad, M. Syaeful., dkk. (2017). Pemodelan Data Kecelakaan Lalu Lintas Menggunakan Metode Regresi Poisson dan Regresi Binomial Negatif. Prosiding Statistika.

Jansakul, N. & J.P Hinde. (2002). Score Test for Zero-Inflated Poisson Models. Computational Statistics & Data Analysis, 40 :75-96.

Lee, Andy H., Wang, Kui. (2006). Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods in Medical Research, No. 15, 47-61.

Mahmoodi, M., dkk. (2016). Semiparametric Models for Multilevel Overdispersed Count Data With Extra Zero. Journal of Statistical Method in Medical Research.

Mardiani, L. K. dkk. (2013). Penerapan Regresi Zero Inflated Poisson Untuk Mengatasi Overdispersi Pada Regresi Poisson. E-Jurnal Matematika Vol. 2, No. 3, 23-28.

McCullagh, P., and J. A. Nelder. (1989). Generalized Linear Models, 2nd Ed. New York: Chapman and Hall

Moustassim, Y., & Ezzahid, E. H. (2012). Poisson Regression and Zero-Inflated Poisson Regression. Aplication to Private Health Insurance Data Vol 2, 187-204.

Tantular, B., Aunuddin & Wijayanto, H., (2009). Pemilihan Model Regresi Linear Multilevel Terbaik. Forum Statistika dan Komputasi.Vol. 14 No. 2.

Wallner, Jon A. (2004). A Semiparametric Regression Model for Panel Count Data: When Do Pseudo-likelihood Estimators Become Badly Inefficient. Proceedings of the Second Seattle Symposium in BioStatistics.

Published
2020-12-30
Section
Articles