Mingqian Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mingqian Zhang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- George To Sum Ho
The Hang Seng University of Hong Kong
1 shared publication - Guo-Liang Tian
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, P. R. China
1 shared publication - Shuang Li
Department of Mathematics, Southern University of Science and Technology, Shenzhen 518055, China
1 shared publication - Xun-Jian Li
Department of Biostatistics
1 shared publication
Latest Publications
- Open Access
- 17 March 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (6) , 1478
Modeling Under-Dispersed Count Data by the Generalized Poisson Distribution via Two New MM Algorithms
Under-dispersed count data often appear in clinical trials, medical studies, demography, actuarial science, ecology, biology, industry and engineering. Although the generalized Poisson (GP) distribution possesses the twin properties of under- and over-dispersion, in the past 50 years, many authors only treat the GP distribution as an alternative to the negative binomial distribution for modeling over-dispersed count data. To our best knowledge, the issues of calculating maximum likelihood estimates (MLEs) of parameters in GP model without covariates and with covariates for the case of under-dispersion were not solved up to now. In this paper, we first develop a new minimization–maximization (MM) algorithm to calculate the MLEs of parameters in the GP distribution with under-dispersion, and then we develop another new MM algorithm to compute the MLEs of the vector of regression coefficients for the GP mean regression model for the case of under-dispersion. Three hypothesis tests (i.e., the likelihood ratio, Wald and score tests) are provided. Some simulations are conducted. The Bangladesh demographic and health surveys dataset is analyzed to illustrate the proposed methods and comparisons with the existing Conway–Maxwell–Poisson regression model are also presented.
- 17 March 2023
- Published by MDPI AG in Mathematics
- Vol. 11 (6) , 1478
Open AccessModeling Under-Dispersed Count Data by the Generalized Poisson Distribution via Two New MM Algorithms
Under-dispersed count data often appear in clinical trials, medical studies, demography, actuarial science, ecology, biology, industry and engineering. Although the generalized Poisson (GP) distribution possesses the twin properties of under- and over-dispersion, in the past 50 years, many authors only treat the GP distribution as an alternative to the negative binomial distribution for modeling over-dispersed count data. To our best knowledge, the issues of calculating maximum likelihood estimates (MLEs) of parameters in GP model without covariates and with covariates for the case of under-dispersion were not solved up to now. In this paper, we first develop a new minimization–maximization (MM) algorithm to calculate the MLEs of parameters in the GP distribution with under-dispersion, and then we develop another new MM algorithm to compute the MLEs of the vector of regression coefficients for the GP mean regression model for the case of under-dispersion. Three hypothesis tests (i.e., the likelihood ratio, Wald and score tests) are provided. Some simulations are conducted. The Bangladesh demographic and health surveys dataset is analyzed to illustrate the proposed methods and comparisons with the existing Conway–Maxwell–Poisson regression model are also presented.