Zero-inflated logit probit model: a novel model for binary data

References

• Bliss, C. I. 1934. The method of probits. Science (New York, N.Y.) 79 (2037):38–9. 17813446 doi: 10.1126/science.79.2037.38.
   PubMedGoogle Scholar
• Bodromurti, W., K. A. Notodiputro, and A. Kurnia. 2018. Zero inflated binomial model for infant mortality data in Indonesia. International Journal of Applied Engineering Research 13 (6):3139–43.
   Google Scholar
• Chakraborty, A., J. Xu, and R. Tiwari. 2022. Zero-inflated binomial model for meta-analysis and safety-signal detection. Therapeutic Innovation & Regulatory Science 56 (2):255–62. 35064554 doi: 10.1007/s43441-021-00353-1.
   PubMed Web of Science ®Google Scholar
• Cox, D. R. 1958. The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological) 20 (2):215–32. doi: 10.1111/j.2517-6161.1958.tb00292.x.
   Web of Science ®Google Scholar
• Diallo, A. O., A. Diop, and J.-F. Dupuy. 2017. Asymptotic properties of the maximum-likelihood estimator in zero-inflated binomial regression. Communications in Statistics- Theory and Methods 46 (20):9930–48. doi: 10.1080/03610926.2016.1222437.
   Web of Science ®Google Scholar
• Diallo, A. O., A. Diop, and J.-F. Dupuy. 2019. Estimation in zero-inflated binomial regression with missing covariates. Statistics 53 (4):839–65. doi: 10.1080/02331888.2019.1619741.
   Web of Science ®Google Scholar
• Diop, A., A. Diop, and J. F. Dupuy. 2011. Maximum likelihood estimation in the logistic regression model with a cure fraction. Electronic Journal of Statistics 5:460–83. doi: 10.1214/11-EJS616.
   Web of Science ®Google Scholar
• Diop, A., A. Diop, and J.-F. Dupuy. 2016. Simulation-based inference in a zero-inflated Bernoulli regression model. Communications in Statistics- Simulation and Computation 45 (10):3597–614. doi: 10.1080/03610918.2014.950743.
   Web of Science ®Google Scholar
• Foutz, R. V. 1977. On the unique consistent solution to the likelihood equations. Journal of the American Statistical Association 72 (357):147–8. doi: 10.1080/01621459.1977.10479926.
   Web of Science ®Google Scholar
• Hall, D. B. 2000. Zero-inflated Poisson and binomial regression with random effects: A case study. Biometrics 56 (4):1030–9. 11129458 doi: 10.1111/j.0006-341x.2000.01030.x.
   PubMed Web of Science ®Google Scholar
• He, H., W. J. Wang, J. Hu, R. Gallop, P. Crits-Christoph, and Y. L. Xia. 2015. Distribution-free inference of zero-inated binomial data for longitudinal studies. Journal of Applied Statistics 42 (10):2203–19. 26435563 doi: 10.1080/02664763.2015.1023270.
   PubMed Web of Science ®Google Scholar
• Lee, S.-M., K.-H. Pho, and C.-S. Li. 2021. Validation likelihood estimation method for a zero-inflated Bernoulli regression model with missing covariates. Journal of Statistical Planning and Inference 214:105–27. doi: 10.1016/j.jspi.2021.01.005.
   Web of Science ®Google Scholar
• Lemonte, A. J., G. Moreno-Arenas, and F. Castellares. 2020. Zero-inflated Bell regression models for count data. Journal of Applied Statistics 47 (2):265–86. 35706517 doi: 10.1080/02664763.2019.1636940.
   PubMed Web of Science ®Google Scholar
• Li, C. S, and M. Lu. 2021. Semiparametric zero-inflated Bernoulli regression with applications. Journal of Applied Statistics 49 (11):2845–69. doi: 10.1080/02664763.2021.1925228.
   PubMed Web of Science ®Google Scholar
• Pho, K. H. 2022. Goodness of fit test for a zero-inflated Bernoulli regression model. Communications in Statistics-Simulation and Computation:1–16. doi: 10.1080/03610918.2022.2032158.
   Web of Science ®Google Scholar
• Pho, K.-H., S. Ly, S. Ly, and T. M. Lukusa. 2019. Comparison among Akaike information criterion, Bayesian information criterion and Vuong’s test in model selection: A case study of violated speed regulation in Taiwan. Journal of Advanced Engineering and Computation 3 (1):293. doi: 10.25073/jaec.201931.220.
   Google Scholar
• Rakitzis, A. C., P. E. Maravelakis, and P. Castagliola. 2016. CUSUM control charts for the monitoring of zero-inflated binomial processes. Quality and Reliability Engineering International 32 (2):465–83. doi: 10.1002/qre.1764.
   Web of Science ®Google Scholar