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Modeling the Cigarette Consumption of Poor Households Using Penalized Zero-Inflated Negative Binomial Regression with Minimax Concave Penalty

Author

Listed:
  • Yudhie Andriyana

    (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Rinda Fitriani

    (National Bureau of Statistics of Blitar Municipality, Blitar 66137, Indonesia)

  • Bertho Tantular

    (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Neneng Sunengsih

    (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Kurnia Wahyudi

    (Department of Medicine, Faculty of Medicine, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • I Gede Nyoman Mindra Jaya

    (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Annisa Nur Falah

    (Doctoral Program of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

Abstract

The cigarette commodity is the second largest contributor to the food poverty line. Several aspects imply that poor people consume cigarettes despite having a minimal income. In this study, we are interested in investigating factors influencing poor people to be active smokers. Since the consumption number is a set of count data with zero excess, we have an overdispersion problem. This implies that a standard Poisson regression technique cannot be implemented. On the other hand, the factors involved in the model need to be selected simultaneously. Therefore, we propose to use a zero-inflated negative binomial (ZINB) regression with a minimax concave penalty (MCP) to determine the dominant factors influencing cigarette consumption in poor households. The data used in this study were microdata from the National Socioeconomic Survey (SUSENAS) conducted in March 2019 in East Java Province, Indonesia. The result shows that poor households with a male head of household, having no education, working in the informal sector, having many adult household members, and receiving social assistance tend to consume more cigarettes than others. Additionally, cigarette consumption decreases with the increasing age of the head of household.

Suggested Citation

  • Yudhie Andriyana & Rinda Fitriani & Bertho Tantular & Neneng Sunengsih & Kurnia Wahyudi & I Gede Nyoman Mindra Jaya & Annisa Nur Falah, 2023. "Modeling the Cigarette Consumption of Poor Households Using Penalized Zero-Inflated Negative Binomial Regression with Minimax Concave Penalty," Mathematics, MDPI, vol. 11(14), pages 1-13, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3192-:d:1198792
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    References listed on IDEAS

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    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    3. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    4. Pampel, Fred, 2008. "Tobacco use in sub-Sahara Africa: Estimates from the demographic health surveys," Social Science & Medicine, Elsevier, vol. 66(8), pages 1772-1783, April.
    5. Abhijeet R Patil & Sangjin Kim, 2020. "Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data," Mathematics, MDPI, vol. 8(1), pages 1-23, January.
    6. Kyung Im Kang & Kyonghwa Kang & Chanhee Kim, 2021. "Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model," IJERPH, MDPI, vol. 18(5), pages 1-12, February.
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