1 |
Agresti A (2020). An Introduction to Categorical Data Analysis, Wiley.
|
2 |
Hall DB (2000). Zero-inflated poisson and binomial regression with random effects: a case study, Biometrics, 54, 1030-1039.
DOI
|
3 |
Kim JT, Lee NY, Oh MA, and Lee SI (2018). A study on the transition of population movement and centroid in Korea, Journal of The Korean Official Statistics, 23, 1-23.
|
4 |
Kim YK and Hwang BS (2018). A Bayesian zero-inflated Poisson regression model with random effects with application to smoking behavior, The Korean Journal of Applied Statistics, 31, 287-301.
DOI
|
5 |
Liu J, Yanyuan M, and Jill J (2020). A goodness-of-fit test for zero-inflated Poisson mixed effects models in tree abundance studies, Computational Statistics & Data Analysis, 144.
|
6 |
Moon, et al. (2020). Time-variant reproductive number of COVID-19 in Seoul, Korea, Epidemiology and Health, 42.
|
7 |
Min Y and Agresti A (2005). Random effect models for repeated measures of zero-inflated count data, Statistical Modelling, 5, 1-19.
DOI
|
8 |
Zhu H, Luo S, and Stacia MD (2015). Zero-inflated count models for longitudinal measurements with heterogeneous random effects, Statistical Methods in Medical Research, 26, 1774-1786.
DOI
|
9 |
Han JH and Kim CH (2015). Zero inflated Poisson model for spatial data, The Korean Journal of Applied Statistics, 28, 231-239.
DOI
|
10 |
Lambert D (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing, Technometrics, 34, 1-14.
DOI
|
11 |
Wang K, Yau KK, and Lee AH (2002). A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays, Computer Methods and Programs in Biomedicine, 68, 195-203.
DOI
|