Modeling Tetanus Neonatorum case using the regression of negative binomial and zero-inflated negative binomial

被引：3
|
作者：
Amaliana, Luthfatul [1 ]
Sa'adah, Umu [1 ]
Wardhani, Ni Wayan Surya [1 ]
机构：
[1] Brawijaya Univ, Jl Vet, Malang 65145, Indonesia
来源：
FIRST AHMAD DAHLAN INTERNATIONAL CONFERENCE ON MATHEMATICS AND MATHEMATICS EDUCATION | 2018年 / 943卷
关键词：
D O I：
10.1088/1742-6596/943/1/012051
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
Tetanus Neonatorum is an infectious disease that can be prevented by immunization. The number of Tetanus Neonatorum cases in East Java Province is the highest in Indonesia until 2015. Tetanus Neonatorum data contain over dispersion and big enough proportion of zero-inflation. Negative Binomial (NB) regression is an alternative method when over dispersion happens in Poisson regression. However, the data containing over dispersion and zero-inflation are more appropriately analyzed by using Zero-Inflated Negative Binomial (ZINB) regression. The purpose of this study are: (1) to model Tetanus Neonatorum cases in East Java Province with 71.05 percent proportion of zero-inflation by using NB and ZINB regression, (2) to obtain the best model. The result of this study indicates that ZINB is better than NB regression with smaller AIC.
引用
收藏
页数：8
相关论文
共 50 条
  • [1] Modeling of Parking Violations Using Zero-Inflated Negative Binomial Regression: A Case Study for Berlin
    Hagen, Tobias
    Reinfeld, Nicole
    Saki, Siavash
    [J]. TRANSPORTATION RESEARCH RECORD, 2023, 2677 (06) : 498 - 512
  • [2] Bivariate zero-inflated negative binomial regression model with applications
    Faroughi, Pouya
    Ismail, Noriszura
    [J]. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2017, 87 (03) : 457 - 477
  • [3] COMPARING POISSON REGRESSION VIA NEGATIVE BINOMIAL REGRESSION FOR MODELING ZERO-INFLATED DATA
    Neamah, Mandi Wahhab
    Albasril, Enas Abid Alhafidh Mohamed
    Raheem, Saif Hosam
    [J]. INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 2021, 17 (01): : 365 - 373
  • [4] Modeling shark bycatch: The zero-inflated negative binomial regression model with smoothing
    Minami, M.
    Lennert-Cody, C. E.
    Gao, W.
    Roman-Verdesoto, M.
    [J]. FISHERIES RESEARCH, 2007, 84 (02) : 210 - 221
  • [5] An alternative bivariate zero-inflated negative binomial regression model using a copula
    So, Sunha
    Lee, Dong-Hee
    Jung, Byoung Cheol
    [J]. ECONOMICS LETTERS, 2011, 113 (02) : 183 - 185
  • [6] Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial regression model
    Zhou, Wanwan
    Huang, Daizheng
    Liang, Qiuyu
    Huang, Tengda
    Wang, Xiaomin
    Pei, Hengyan
    Chen, Shiwen
    Liu, Lu
    Wei, Yuxia
    Qin, Litai
    Xie, Yihong
    [J]. BMC INFECTIOUS DISEASES, 2024, 24 (01)
  • [7] On estimation and influence diagnostics for zero-inflated negative binomial regression models
    Garay, Aldo M.
    Hashimoto, Elizabeth M.
    Ortega, Edwin M. M.
    Lachos, Victor H.
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2011, 55 (03) : 1304 - 1318
  • [8] A zero-inflated negative binomial regression model with hidden Markov chain
    Wang, Peiming
    Alba, Joseph D.
    [J]. ECONOMICS LETTERS, 2006, 92 (02) : 209 - 213
  • [9] Improved shrinkage estimators in zero-inflated negative binomial regression model
    Zandi, Zahra
    Bevrani, Hossein
    Belaghi, Reza Arabi
    [J]. HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, 2021, 50 (06): : 1855 - 1876
  • [10] Marginalized zero-inflated negative binomial regression with application to dental caries
    Preisser, John S.
    Das, Kalyan
    Long, D. Leann
    Divaris, Kimon
    [J]. STATISTICS IN MEDICINE, 2016, 35 (10) : 1722 - 1735
12345