Browse > Article
http://dx.doi.org/10.29220/CSAM.2022.29.4.413

Modeling clustered count data with discrete weibull regression model  

Yoo, Hanna (Department of Big Data, Busan University of Foreign Studies)
Publication Information
Communications for Statistical Applications and Methods / v.29, no.4, 2022 , pp. 413-420 More about this Journal
Abstract
In this study we adapt discrete weibull regression model for clustered count data. Discrete weibull regression model has an attractive feature that it can handle both under and over dispersion data. We analyzed the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the 1 month outpatient stay in 17 different regions. We compared the results using clustered discrete Weibull regression model with those of Poisson, negative binomial, generalized Poisson and Conway-maxwell Poisson regression models, which are widely used in count data analyses. The results show that the clustered discrete Weibull regression model using random intercept model gives the best fit. Simulation study is also held to investigate the performance of the clustered discrete weibull model under various dispersion setting and zero inflated probabilities. In this paper it is shown that using a random effect with discrete Weibull regression can flexibly model count data with various dispersion without the risk of making wrong assumptions about the data dispersion.
Keywords
clustered count data; discrete weibull regression; dispersion; random effect;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Michas F (2021). Number of doctor visits per capita in selected countries 2019. Statista, Health Professionals Hospitals. Available from: https://www.statista.com/statistics/236589/number-of-doctor-visits-per-capita-by-country/#statisticContainer
2 Alan B and Nial F (2021). Bayesian inference, model selection and likelihood estimation using fast rejection sampling: The Conway-Maxwell-Poisson distribution, Bayesian Analysis, 16, 905-931.
3 Barbiero A (2019). A bivariate count model with discrete Weibull margins, Mathematics and Computers in Simulation, 156, 91-109.   DOI
4 Dunlop D (1994). Regression for longitudinal data: A bridge from least squares regression, The American Statistician, 48, 299-303.
5 Klakattawi HS, Vinciotti V, and Yu K (2018). A simple and adaptive dispersion regression model for count data, Entropy, 20, 142.
6 Kulasekera K (1994). Approximate MLE's of the parameters of a discrete Weibull distribution with type 1 censored data.Microelectron, Reliab, 34, 1185-1188.   DOI
7 Lee S (2004). Development of a Sustainable Health Management System for Management of Chronic Diseases. Health and Welfare Policy Forum 1, 72-81.
8 Lee Y and Nelder AJ (1996).Hierarchical generalized linear models, Journal of the Royal Statistical Society. Series B (Methodological), 58, 619-678.   DOI
9 Yau KKW, Wang K, and Lee AH (2003). Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros, Biometrics, 45, 437-452.   DOI
10 Luyts M, Geert M, Geert V, Koen M, Eduardo R, Clarice D, and John H. (2019). A Weibull-count approach for handling under- and overdispersed longitudinal/clustered data structures, Statistical Modelling, 19, 569-589.   DOI
11 Albert J (1992). A Bayesian analysis of a Poisson random effects model for home run hitters, The American Statistician, 46, 246-253.
12 Hall DB (2000). Zero-inflated Poisson and binomial regression with random effects: A case study, Biometrics, 56, 1030-1039.   DOI
13 Lawless J (1987). Negative binomial and mixed Poisson regression, Canadian Journal of Statistics, 15, 209-225.   DOI
14 Nakagawa T and Osaki S (1975). The discrete Weibull distribution.IEEE Transactions on Reliability , 24, 300-301.   DOI