Comparison of Zero Inflated Poisson (ZIP) Regression, Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB) to Model Daily Cigarette Consumption Data for Adult Population in Indonesia


  • Drajat Indra Purnama Badan Pusat Statistik Kabupaten Parigi Moutong, Sulawesi Tengah



ZIP, ZINB, HNB, Cigarette Consumption


Smoking is a habit that is not good for health. Smoking habits are generally practiced by adults but it is possible for teenagers to do so.The Report of Southeast Asia Tobacco Control Alliance (SEATCA) entitled The Tobacco Control Atlas, ASEAN Region shows that Indonesia is the country with the highest number of smokers in ASEAN, namely 65.19 million people. This figure is equivalent to 34 percent of the total population of Indonesia in 2016. Based on these data, the authors are interested in modeling the daily cigarette consumption data for adults in Indonesia obtained from the 2015 Indonesia Family Life Survey. The variables used include the variable amount of cigarette consumption, education, level of welfare and income per month. The author wants to compare the best model that can be used to model the daily cigarette consumption of adults in Indonesia. The models being compared are Zero Inflated Poisson Regression (ZIP), Zero Inflated Negative Binomial Regression (ZINB) and Binomial Negative Hurdle Regression (HNB). The comparison results of the three models obtained that the best model is the Zero Inflated Negative Binomial (ZINB) Regression model because it has the smallest Akaike's Information Criterion (AIC) value.


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. Annisa, R., Permatasari, E.O., and Rumiati, A.T. 2020. Modeling of infant mortality in west sulawesi using zero inflated poisson regression method. Journal of Physics: Conference Series 1490.

. Badan Pusat Statistik. 2020. Statistik Indonesia 2020. Jakarta : Badan Pusat Statistik.

. Badan Pusat Statistik. 2020. Statistik Kesejahteraan Rakyat 2020. Jakarta : Badan Pusat Statistik.

. Cameron, A.C. and Trivedi, P.K., 1998. Regression Analysis of Count Data. New York : Cambridge University Press.

. Fang, R., Wagner, B.D., Harris, J.K., and Fillon, S.A. 2016. Zero-inflated negative binomial mixed model: an application to two microbial organisms important in oesophagitis. Epidemiol Infect 144 : 2447–2455.

. Gujarati, D. 2009. Dasar-Dasar Ekonometrika Jilid 2. Jakarta: Erlangga.

. Hilbe, J.M. 2011. Negative Binomial Regression, Second Edition. New York: Cambrigde University Press.

. Hosmer, D.W. and Lemeshow, S. 2000. Applied Logistic Regression. New York: John Wiley & Sons, Inc.

. Jansakul, N. and Hinde, J.P. 2002. Score Tests for Zero-Inflated Poisson Models. Computational Statistics & Data Analysis. Vol. 40 No.1 : 75-96.

. Myers, R.H. 1990. Classical and Modern Regression with Appilications, 2nd ed. Boston: PW-KENT Publishing Company Boston.

. Myers, R.H., Montgomery, D.C, Vining, G.G. and Robinson, T.J. 2010. Generalized Linear Models with Applications in Engineering and The Sciences, Second edition. New Jersey: John Wiley and Sons.

. Nargis, N., Yong, H.H., Driezen, P., et al. 2019. Socioeconomic patterns of smoking cessation behavior in low and middle-income countries: Emerging evidence from the Global Adult Tobacco Surveys and International Tobacco Control Surveys. PLoS ONE 14(9): e0220223.

. Osuji, G. A., Okoro, C. N., Obubu, M. and Obiora-Ilouno, H. O. 2016. Effect of Akaike Information Criterion on Model Selection in Analyzing Auto-crash Variables. International Journal of Sciences: Basic and Applied Research (IJSBAR). Volume 26 No 1 : 98-109.

. Wang, Q., Shen, J.J., Sotero, M., et al. 2018. Income, occupation and education : Are they related to smoking behaviors in China?. PLoS ONE 13(2): e0192571.

. Zhen, Z., Shao, L. and Zhang, L. 2018. Spatial Hurdle Models for Predicting the Number of

Children with Lead Poisoning. International Journal of Environmental Research and Public Health 15.






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