DOI: https://doi.org/10.14710/medstat.16.2.206-214

HANDLING OF OVERDISPERSION CASES IN MORBIDITY DATA IN SELUMA REGENCY

*Mey Yanti Sarumpaet  -  Statistics Study Program, The University of Bengkulu, Indonesia
Sigit Nugroho  -  Statistics Study Program, The University of Bengkulu, Indonesia
Ramya Rachmawati  -  Statistics Study Program, The University of Bengkulu, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
The problem of overdispersion as a violation of the assumption of equidispersion in Poisson regression is generally caused by  sources of unobserved heterogeneity, missing observations on predictor variables, outliers in the data, errors in the specification of the bridging function, and many observed  values that are zero.  The  purpose of  this study is  to find out the right  model and the variables  that affect data that occurs overdispersion and excess zero in the case of the number of days of disruption at work, school, or other daily activities due to health complaints. The methods used were Poisson Regression, Negative  Binomial Regression, Hurdle  Poisson  Regression,  Zero  Inflated Poisson Regression,  Zero  Inflated  Negative  Binomial Regression, and Hurdle Negative Binomial Regression. The data used were morbidity taken from data on the number of days  of  disruption at  work,  school  or  other daily  activities due  to  health  complaints  in  Seluma district,  Bengkulu Province. It was found that the best model is Zero Inflated Negative  Poisson  with  the  smallest  Akaike  Information Criterion (AIC) value of 1620.609  and the variables that have  a  significant  effect on the  log model and the logit model are marital status and work variables.
Fulltext View|Download
Keywords: Overdispersion, Poisson, Negative Binomial, Zero Inflated, Hurdle, Morbidity

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
MAKING BAYESIAN DISEASE MAPPING EASY AND INTERACTIVE: AN R SHINY APPLICATION BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) AND LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) MODELS MODELING OF WORLD CRUDE OIL PRICE BASED ON PULSE FUNCTION INTERVENTION ANALYSIS APPROACH More recent articles
Most cited articles
DISTRIBUSI RAYLEIGH UNTUK KLAIM AGREGASI ANALISIS DATA INFLASI DI INDONESIA MENGGUNAKAN MODEL REGRESI SPLINE PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION Front-Matter ANALISIS PENGARUH LOKASI DAN KARAKTERISTIK KONSUMEN DALAM MEMILIH MINIMARKET DENGAN METODE REGRESI LOGISTIK DAN CART More cited articles
  1. Ariawan, B., Suparti, & Sudarno. (2012). Pemodelan Regresi Zero-Inflated Negative Binomial (ZINB) untuk Data Respon Diskrit dengan Excess Zeros. Jurnal Gaussian, 1(1)
  2. Broek J. 1995. A Score test of for Zero Inflation In a Poisson Distribution. Biometrics. 51: 738-743. https://doi.org/10.2307/2532959
  3. Desjardins, C. D. (2014). Evaluating the performance of two competing models of school suspension under simulation - the zero-inflated negative binomial and the negative binomial hurdle. Dissertation Abstracts International Section A: Humanities and Social Sciences, 74(10-A(E))
  4. Eunice, A., Wanjoya, A., & Luboobi, L. (2017). Statistical Modeling of Malaria Incidences in Apac District, Uganda. Open Journal of Statistics, 07(06), 901–919. https://doi.org/10.4236/ojs.2017.76063
  5. Fitriani, R., Chrisdiana, L. N., & Efendi, A. (2019). Simulation on the Zero Inflated Negative Binomial (ZINB) to Model Overdispersed, Poisson Distributed Data. IOP Conference Series: Materials Science and Engineering, 546(5), 52025. https://doi.org/10.1088/1757-899X/546/5/052025
  6. Garay, A., Hashimoto, E., Lachos, V., & Ortega, E. 2011. On Estimation and Influence Diagnostics for Zero-Inflated Negative Binomial Regression Models. Computational Statistics and Data Analysis, 55, 1304-1318
  7. Greene, W. (2008). Functional forms for the negative binomial model for count data. Economics Letters, 99(3). https://doi.org/10.1016/j.econlet.2007.10.015
  8. Hardin, J. W., & Hilbe, J. M. (2008). Analysis of fit. In Generalized Linear Models and Extensions, Second Edition (Vol. 2)
  9. Hilbe, J.M. 2011. Negative Binomial Regression (2th ed.). New York : Cambridge University Press
  10. Hu, M. C., Pavlicova, M., & Nunes, E. V. (2011). Zero-inflated and hurdle models of count data with extra zeros: Examples from an HIV-risk reduction intervention trial. American Journal of Drug and Alcohol Abuse, 37(5). https://doi.org/10.3109/00952990.2011.597280
  11. Jansakul, N. & J.P. Hinde. 2002. Score Tests for Zero-Inflated Poisson Models
  12. Computational Statistics & Data Analysis, 40: 75-96
  13. Mc.Cullagh, P. & J.A. Nelder. 1989. Generalized Linear Models (2th ed). London: Chapman and Hall
  14. Saffari, S. E., Adnan, R., & Greene, W. (2012). Parameter estimation on hurdle poisson regression model with censored data. Jurnal Teknologi (Sciences and Engineering), 57(SUPPL.1). https://doi.org/10.11113/jt.v57.1533
  15. Sreelatha, C. H., & Muniswamy, B. (2018). A WALD TEST FOR OVER DISPERSION IN ZERO-INFLATED POISSON REGRESSION MODEL. In International Journal of Mathematical Archive (Vol. 9, Issue 6, pp. 201–212). www.ijma.info
  16. Taufan M., Suparti, & Agus R., 2012. Analisi Faktor-Faktor yang Mempengarhi Banyaknya Klaim Assuransi Kendaraan Bermotor Menggunakan Model Regresi Zero-Inflated Poisson (Studi Kasus di PT. Asuransi Sinar Mas Cabang Semarang Tahun 2010). Media Staristika, 5(1): 49-61
  17. Winkelman, R. 2008. Econometric Analysis of Count Data 5th edition. Berlin: Springer
  18. Yang, S., Puggioni, G., Harlow, L. L., & Redding, C. A. (2017). A comparison of different methods of zero - inflated data analysis and an application in health surveys. Journal of Modern Applied Statistical Methods, 16(1). https://doi.org/10.22237/jmasm/1493598600

Last update:

No citation recorded.

Last update: 2024-11-23 01:50:19

No citation recorded.