COUNT

R package COUNT: Functions, Data and Code for Count Data. Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

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References in zbMATH (referenced in 49 articles )

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  1. Bagchi, Aniruddha; Paul, Jomon A.: National security vs. human rights: a game theoretic analysis of the tension between these objectives (2021)
  2. Peyhardi, Jean; Fernique, Pierre; Durand, Jean-Baptiste: Splitting models for multivariate count data (2021)
  3. Daghyani, Masoud; Zamzami, Nuha; Bouguila, Nizar: Toward an efficient computation of log-likelihood functions in statistical inference: overdispersed count data clustering (2020)
  4. Hwang, Wen-Han; Blakey, Rachel V.; Stoklosa, Jakub: Right-censored mixed Poisson count models with detection times (2020)
  5. Jourdain, N. O. A. S.; Cole, D. J.; Ridout, M. S.; Rowcliffe, J. Marcus: Statistical development of animal density estimation using random encounter modelling (2020)
  6. Kowal, Daniel R.; Canale, Antonio: Simultaneous transformation and rounding (STAR) models for integer-valued data (2020)
  7. Rydén, Jesper: On features of fugue subjects. A comparison of J. S. Bach and later composers (2020)
  8. Weißbach, Rafael; Radloff, Lucas: Consistency for the negative binomial regression with fixed covariate (2020)
  9. Amiri, Mohammad Moqaddasi; Tapak, Leili; Faradmal, Javad: A mixed-effects least square support vector regression model for three-level count data (2019)
  10. Borges, Patrick; Godoi, Luciana G.: Pólya-Aeppli regression model for overdispersed count data (2019)
  11. Çetinkaya, Merve Kandemir; Kaçıranlar, Selahattin: Improved two-parameter estimators for the negative binomial and Poisson regression models (2019)
  12. Kim, Jeonghwan; Lee, Woojoo: On testing the hidden heterogeneity in negative binomial regression models (2019)
  13. Li, Chin-Shang; Lee, Shen-Ming; Yeh, Ming-Shan: A test for lack-of-fit of zero-inflated negative binomial models (2019)
  14. Luyts, Martial; Molenberghs, Geert; Verbeke, Geert; Matthijs, Koen; Ribeiro, Eduardo E. jun.; Demétrio, Clarice G. B.; Hinde, John: A Weibull-count approach for handling under- and overdispersed longitudinal/clustered data structures (2019)
  15. Martínez-Rodríguez, Ana María; Conde-Sánchez, Antonio; Olmo-Jiménez, María José: A new approach to truncated regression for count data (2019)
  16. Xiong, Lanyu; Zhu, Fukang: Robust quasi-likelihood estimation for the negative binomial integer-valued GARCH(1,1) model with an application to transaction counts (2019)
  17. Yamrubboon, Darika; Thongteeraparp, Ampai; Bodhisuwan, Winai; Jampachaisri, Katechan; Volodin, Andrei: Bayesian inference for the negative binomial-Sushila linear model (2019)
  18. Adragni, Kofi P.: Minimum average deviance estimation for sufficient dimension reduction (2018)
  19. Asar, Yasin: Liu-type negative binomial regression: a comparison of recent estimators and applications (2018)
  20. Asar, Yasin; Genç, Aşır: A new two-parameter estimator for the Poisson regression model (2018)

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