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A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution

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Cited by:

  1. Abdolnasser Sadeghkhani & Seyed Ejaz Ahmed, 2019. "A Bayesian Approach to Predict the Number of Goals in Hockey," Stats, MDPI, vol. 2(2), pages 1-11, April.
  2. Vicente Cancho & Mário Castro & Josemar Rodrigues, 2012. "A Bayesian analysis of the Conway–Maxwell–Poisson cure rate model," Statistical Papers, Springer, vol. 53(1), pages 165-176, February.
  3. Pattiz, Brian David, 2009. "Count regression models for recreation demand: an application to Clear Lake," ISU General Staff Papers 200901010800002092, Iowa State University, Department of Economics.
  4. John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
  5. Orasa Anan & Dankmar Böhning & Antonello Maruotti, 2019. "On the Turing estimator in capture–recapture count data under the geometric distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 149-172, March.
  6. Siddharth Singh & Sharad Borle & Dipak Jain, 2009. "A generalized framework for estimating customer lifetime value when customer lifetimes are not observed," Quantitative Marketing and Economics (QME), Springer, vol. 7(2), pages 181-205, June.
  7. Marcelo Bourguignon & Rodrigo M. R. Medeiros, 2022. "A simple and useful regression model for fitting count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 790-827, September.
  8. Bilal Barakat, 2017. "Generalised count distributions for modelling parity," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 36(26), pages 745-758.
  9. Sellers, Kimberly F. & Morris, Darcy Steeg & Balakrishnan, Narayanaswamy, 2016. "Bivariate Conway–Maxwell–Poisson distribution: Formulation, properties, and inference," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 152-168.
  10. Sellers, Kimberly F. & Raim, Andrew, 2016. "A flexible zero-inflated model to address data dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 68-80.
  11. Huaping Chen, 2023. "A New Soft-Clipping Discrete Beta GARCH Model and Its Application on Measles Infection," Stats, MDPI, vol. 6(1), pages 1-19, February.
  12. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
  13. Zhou, Can & Jiao, Yan & Browder, Joan, 2019. "K-aggregated transformation of discrete distributions improves modeling count data with excess ones," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
  14. Garrahan, Juan P., 2018. "Aspects of non-equilibrium in classical and quantum systems: Slow relaxation and glasses, dynamical large deviations, quantum non-ergodicity, and open quantum dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 504(C), pages 130-154.
  15. Kimberly F. Sellers & Andrew W. Swift & Kimberly S. Weems, 2017. "A flexible distribution class for count data," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-21, December.
  16. Mevin B. Hooten & Michael R. Schwob & Devin S. Johnson & Jacob S. Ivan, 2023. "Multistage hierarchical capture–recapture models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
  17. Can Zhou & Yan Jiao & Joan Browder, 2019. "How much do we know about seabird bycatch in pelagic longline fisheries? A simulation study on the potential bias caused by the usually unobserved portion of seabird bycatch," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
  18. Chedly Gelin Louzayadio & Rodnellin Onesime Malouata & Michel Diafouka Koukouatikissa, 2021. "A Weighted Poisson Distribution for Underdispersed Count Data," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(4), pages 157-157, July.
  19. S. Hadi Khazraee & Antonio Jose Sáez‐Castillo & Srinivas Reddy Geedipally & Dominique Lord, 2015. "Application of the Hyper‐Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes," Risk Analysis, John Wiley & Sons, vol. 35(5), pages 919-930, May.
  20. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
  21. Joseph V. Hackman & Karen L. Kramer, 2021. "Kin Ties and Market Integration in a Yucatec Mayan Village," Social Sciences, MDPI, vol. 10(6), pages 1-17, June.
  22. Kimberly F. Sellers & Tong Li & Yixuan Wu & Narayanaswamy Balakrishnan, 2021. "A Flexible Multivariate Distribution for Correlated Count Data," Stats, MDPI, vol. 4(2), pages 1-19, April.
  23. Rufin Bidounga & Evrand Giles Brunel Mandangui Maloumbi & Réolie Foxie Mizélé Kitoti & Dominique Mizère, 2020. "The New Bivariate Conway-Maxwell-Poisson Distribution Obtained by the Crossing Method," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 9(6), pages 1-1, November.
  24. Sophia Lin & Take Naseri & Christine Linhart & Stephen Morrell & Richard Taylor & Stephen T. Mcgarvey & Dianna J. Magliano & Paul Zimmet, 2017. "Diabetes incidence and projections from prevalence surveys in Samoa over 1978–2013," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(6), pages 687-694, July.
  25. Bedbur, S. & Kamps, U., 2023. "Uniformly most powerful unbiased tests for the dispersion parameter of the Conway–Maxwell–Poisson distribution," Statistics & Probability Letters, Elsevier, vol. 196(C).
  26. Fernando Bonassi & Rafael Stern & Cláudia Peixoto & Sergio Wechsler, 2015. "Exchangeability and the law of maturity," Theory and Decision, Springer, vol. 78(4), pages 603-615, April.
  27. Yan Cui & Qi Li & Fukang Zhu, 2020. "Flexible bivariate Poisson integer-valued GARCH model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(6), pages 1449-1477, December.
  28. Suvra Pal & Jacob Majakwara & N. Balakrishnan, 2018. "An EM algorithm for the destructive COM-Poisson regression cure rate model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(2), pages 143-171, February.
  29. Douglas Toledo & Cristiane Akemi Umetsu & Antonio Fernando Monteiro Camargo & Idemauro Antonio Rodrigues Lara, 2022. "Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 473-497, September.
  30. Saralees Nadarajah, 2009. "Useful moment and CDF formulations for the COM–Poisson distribution," Statistical Papers, Springer, vol. 50(3), pages 617-622, June.
  31. Ramesh Gupta & S. Sim & S. Ong, 2014. "Analysis of discrete data by Conway–Maxwell Poisson distribution," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(4), pages 327-343, October.
  32. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
  33. Chatla, Suneel Babu & Shmueli, Galit, 2018. "Efficient estimation of COM–Poisson regression and a generalized additive model," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 71-88.
  34. Seng Huat Ong & Shin Zhu Sim & Shuangzhe Liu & Hari M. Srivastava, 2023. "A Family of Finite Mixture Distributions for Modelling Dispersion in Count Data," Stats, MDPI, vol. 6(3), pages 1-14, September.
  35. María Alonso‐Pena & Irène Gijbels & Rosa M. Crujeiras, 2023. "Flexible joint modeling of mean and dispersion for the directional tuning of neuronal spike counts," Biometrics, The International Biometric Society, vol. 79(4), pages 3431-3444, December.
  36. Morris, Darcy Steeg & Raim, Andrew M. & Sellers, Kimberly F., 2020. "A Conway–Maxwell-multinomial distribution for flexible modeling of clustered categorical data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
  37. Dexter Cahoy & Elvira Di Nardo & Federico Polito, 2021. "Flexible models for overdispersed and underdispersed count data," Statistical Papers, Springer, vol. 62(6), pages 2969-2990, December.
  38. Pogány, Tibor K., 2016. "Integral form of the COM–Poisson renormalization constant," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 144-145.
  39. Aamir Saghir & Zhengyan Lin, 2014. "Control chart for monitoring multivariate COM-Poisson attributes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 200-214, January.
  40. Krivitsky, Pavel N., 2017. "Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 149-161.
  41. Sharad Borle & Utpal M. Dholakia & Siddharth S. Singh & Robert A. Westbrook, 2007. "The Impact of Survey Participation on Subsequent Customer Behavior: An Empirical Investigation," Marketing Science, INFORMS, vol. 26(5), pages 711-726, 09-10.
  42. Qi Li & Fukang Zhu, 2020. "Mean targeting estimator for the integer-valued GARCH(1, 1) model," Statistical Papers, Springer, vol. 61(2), pages 659-679, April.
  43. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
  44. Balakrishnan, N. & Pal, Suvra, 2013. "Lognormal lifetimes and likelihood-based inference for flexible cure rate models based on COM-Poisson family," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 41-67.
  45. Mamode Khan Naushad & Rumjaun Wasseem & Sunecher Yuvraj & Jowaheer Vandna, 2017. "Computing with bivariate COM-Poisson model under different copulas," Monte Carlo Methods and Applications, De Gruyter, vol. 23(2), pages 131-146, June.
  46. Royce A. Francis & Srinivas Reddy Geedipally & Seth D. Guikema & Soma Sekhar Dhavala & Dominique Lord & Sarah LaRocca, 2012. "Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model," Risk Analysis, John Wiley & Sons, vol. 32(1), pages 167-183, January.
  47. Orasa Anan & Dankmar Böhning & Antonello Maruotti, 2017. "Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 49-79, March.
  48. Moizes Melo & Airlane Alencar, 2020. "Conway–Maxwell–Poisson Autoregressive Moving Average Model for Equidispersed, Underdispersed, and Overdispersed Count Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 830-857, November.
  49. Seyed Ehsan Saffari & John Carson Allen & Robiah Adnan & Seng Huat Ong & Shin Zhu Sim & William Greene, 2019. "Frequency of Visiting a Doctor: A right Truncated Count Regression Model with Excess Zeros," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 112-122, August.
  50. Kimberly F. Sellers & Ali Arab & Sean Melville & Fanyu Cui, 2021. "A flexible univariate moving average time-series model for dispersed count data," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-12, December.
  51. Muhammad Aslam & Ali Hussein Al-Marshadi, 2019. "Design of a Control Chart Based on COM-Poisson Distribution for the Uncertainty Environment," Complexity, Hindawi, vol. 2019, pages 1-9, July.
  52. Sáez-Castillo, A.J. & Conde-Sánchez, A., 2013. "A hyper-Poisson regression model for overdispersed and underdispersed count data," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 148-157.
  53. Rodrigues, Josemar & Bazán, Jorge L. & Suzuki, Adriano K. & Balakrishnan, Narayanaswamy, 2016. "The Bayesian restricted Conway–Maxwell-Binomial model to control dispersion in count data," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 281-288.
  54. Mark S. Handcock & Krista J. Gile & Corinne M. Mar, 2015. "Estimating the size of populations at high risk for HIV using respondent-driven sampling data," Biometrics, The International Biometric Society, vol. 71(1), pages 258-266, March.
  55. Joseph B. Kadane & Ramayya Krishnan & Galit Shmueli, 2006. "A Data Disclosure Policy for Count Data Based on the COM-Poisson Distribution," Management Science, INFORMS, vol. 52(10), pages 1610-1617, October.
  56. Rodrigues, Josemar & Balakrishnan, N. & Cordeiro, Gauss M. & de Castro, Mário, 2011. "A unified view on lifetime distributions arising from selection mechanisms," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3311-3319, December.
  57. Robert E. Gaunt & Satish Iyengar & Adri B. Olde Daalhuis & Burcin Simsek, 2019. "An asymptotic expansion for the normalizing constant of the Conway–Maxwell–Poisson distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(1), pages 163-180, February.
  58. Subrata Chakraborty & S. H. Ong, 2017. "Mittag - Leffler function distribution - a new generalization of hyper-Poisson distribution," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-17, December.
  59. N. Balakrishnan & Suvra Pal, 2015. "An EM algorithm for the estimation of parameters of a flexible cure rate model with generalized gamma lifetime and model discrimination using likelihood- and information-based methods," Computational Statistics, Springer, vol. 30(1), pages 151-189, March.
  60. Dominique Lord & Srinivas Reddy Geedipally & Seth D. Guikema, 2010. "Extension of the Application of Conway‐Maxwell‐Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1268-1276, August.
  61. Xun-Jian Li & Guo-Liang Tian & Mingqian Zhang & George To Sum Ho & Shuang Li, 2023. "Modeling Under-Dispersed Count Data by the Generalized Poisson Distribution via Two New MM Algorithms," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
  62. Seth D. Guikema & Jeremy P. Goffelt, 2008. "A Flexible Count Data Regression Model for Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 28(1), pages 213-223, February.
  63. Adeniyi, Isaac Adeola, 2020. "Bayesian Generalized Linear Mixed Effects Models Using Normal-Independent Distributions: Formulation and Applications," MPRA Paper 99165, University Library of Munich, Germany.
  64. Meena Badade & T. V. Ramanathan, 2022. "Probabilistic Frontier Regression Models for Count Type Output Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 235-260, September.
  65. Sharad Borle & Peter Boatwright & Joseph B. Kadane & Joseph C. Nunes & Shmueli Galit, 2005. "The Effect of Product Assortment Changes on Customer Retention," Marketing Science, INFORMS, vol. 24(4), pages 616-622, July.
  66. Sharad Borle & Siddharth S. Singh & Dipak C. Jain, 2008. "Customer Lifetime Value Measurement," Management Science, INFORMS, vol. 54(1), pages 100-112, January.
  67. Rajib Dey & M. Ataharul Islam, 2017. "A conditional count model for repeated count data and its application to GEE approach," Statistical Papers, Springer, vol. 58(2), pages 485-504, June.
  68. Borges, Patrick & Rodrigues, Josemar & Balakrishnan, Narayanaswamy & Bazán, Jorge, 2014. "A COM–Poisson type generalization of the binomial distribution and its properties and applications," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 158-166.
  69. Kirthi Kalyanam & Sharad Borle & Peter Boatwright, 2007. "Deconstructing Each Item's Category Contribution," Marketing Science, INFORMS, vol. 26(3), pages 327-341, 05-06.
  70. Ehab M. Almetwally & Sanku Dey & Saralees Nadarajah, 2023. "An Overview of Discrete Distributions in Modelling COVID-19 Data Sets," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1403-1430, August.
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