A novel model for count data: zero-inflated Probit Bell model with applications

被引:0
|
作者
Ali, Essoham [1 ,2 ]
Pho, Kim-Hung [3 ]
机构
[1] Univ Bretagne Sud, CNRS, UMR 6205, LMBA, Vannes, France
[2] UCO, Inst Math Appl, Angers, France
[3] Ton Duc Thang Univ, Fac Math & Stat, Fract Calculus Optimizat & Algebra Res Grp, Ho Chi Minh City, Vietnam
关键词
Estimation; Models; Probit; Regression; Simulation; Zero-inflated bell; POISSON REGRESSION;
D O I
10.1080/03610918.2024.2384574
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
A novel model for count data is proposed in this work. This new model is briefly called the Zero-Inflated Probit Bell (ZIPBell) model. The ZIPBell model can be used to simulate and analyze traditional count data and can be used well in the simulation and analysis of count data with the phenomenon that the frequency for zero occupies a very high percentage. In the scope of this study, we first provide the general formula, functions or related equations for the ZIPBell model. The results of asymptotic inferences for the ZIPBell model are also given in this work. Maximum likelihood estimator (MLE) is used to check its performance of the proposed model. According to the numerical illustrations via several simulation researches and the analysis of a practical data set, it has been seen that the MLE method has offered correct and reliable inferences. Finally, the inferences and conclusions of this study are mentioned.
引用
收藏
页数:19
相关论文
共 50 条
  • [41] Marginal zero-inflated regression models for count data
    Martin, Jacob
    Hall, Daniel B.
    [J]. JOURNAL OF APPLIED STATISTICS, 2017, 44 (10) : 1807 - 1826
  • [42] Modeling count data with marginalized zero-inflated distributions
    Cummings, Tammy H.
    Hardin, James W.
    [J]. STATA JOURNAL, 2019, 19 (03): : 499 - 509
  • [43] Zero-inflated models with application to spatial count data
    Deepak K. Agarwal
    Alan E. Gelfand
    Steven Citron-Pousty
    [J]. Environmental and Ecological Statistics, 2002, 9 : 341 - 355
  • [44] Semiparametric analysis of longitudinal zero-inflated count data
    Feng, Jiarui
    Zhu, Zhongyi
    [J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2011, 102 (01) : 61 - 72
  • [45] Zero-inflated models with application to spatial count data
    Agarwal, DK
    Gelfand, AE
    Citron-Pousty, S
    [J]. ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 2002, 9 (04) : 341 - 355
  • [46] Marginal Mean Models for Zero-Inflated Count Data
    Todem, David
    Kim, KyungMann
    Hsu, Wei-Wen
    [J]. BIOMETRICS, 2016, 72 (03) : 986 - 994
  • [47] A Flexible Zero-Inflated Poisson-Gamma Model with Application to Microbiome Sequence Count Data
    Jiang, Roulan
    Zhan, Xiang
    Wang, Tianying
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2023, 118 (542) : 792 - 804
  • [48] Modeling zero-inflated count data using a covariate-dependent random effect model
    Wong, Kin-Yau
    Lam, K. F.
    [J]. STATISTICS IN MEDICINE, 2013, 32 (08) : 1283 - 1293
  • [49] Time Series Regression for Zero-Inflated and Overdispersed Count Data: A Functional Response Model Approach
    M. Ghahramani
    S. S. White
    [J]. Journal of Statistical Theory and Practice, 2020, 14
  • [50] On Baseline Conditions for Zero-Inflated Longitudinal Count Data
    Maruotti, Antonello
    Raponi, Valentina
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2014, 43 (04) : 743 - 760