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Articles

Zero-inflated sum of Conway-Maxwell-Poissons (ZISCMP) regression

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Pages 1649-1673 | Received 21 Dec 2018, Accepted 28 Feb 2019, Published online: 15 Mar 2019

References

  • Lambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1–14. doi: 10.2307/1269547
  • Greene WH. Accounting for excess zeros and sample selection in poisson and negative binomial regression models. SSRN eLibrary. 1994.
  • Hall DB. Zero-inflated Poisson and binomial regression with random effects: a case study. Biometrics. 2000;56(4):1030–1039. doi: 10.1111/j.0006-341X.2000.01030.x
  • Yau KKW, Wang K, Lee AH. Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros. Biom J. 2003;45(4):437–452. doi: 10.1002/bimj.200390024
  • Barriga GDC, Louzada F. The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic. Stat Methodol. 2014;21:23–34. doi: 10.1016/j.stamet.2013.11.003
  • Sellers KF, Raim A. A flexible zero-inflated model to address data dispersion. Comput Stat Data Anal. 2016;99:68–80. doi: 10.1016/j.csda.2016.01.007
  • Consul PC, Jain GC. A generalization of the Poisson distribution. Technometrics. 1973;15(4):791–799. doi: 10.1080/00401706.1973.10489112
  • Consul PC. Generalized poisson distributions: properties and applications. New York (NY): Marcel Dekker; 1989.
  • Famoye F, Singh KP. Zero-inflated generalized Poisson regression model with an application to domestic violence data. J Data Sci. 2006;4:117–130.
  • Sellers KF, Swift AW, Weems KS. sCOM-Poisson: a flexible count distribution to address dispersion in count data. J Stat Distrib Appl. 2017;4(22):1–7.
  • Conway RW, Maxwell WL. A queuing model with state dependent service rates. J Ind Eng. 1962;12:132–136.
  • Shmueli G, Minka TP, Kadane JB, et al. A useful distribution for fitting discrete data: revival of the Conway-Maxwell-Poisson distribution. Appl Stat. 2005;54:127–142.
  • Sellers KF, Shmueli G, Borle S. The COM-Poisson model for count data: a survey of methods and applications. Appl Stoch Models Bus Ind. 2011;28:104–116. doi: 10.1002/asmb.918
  • Balakrishnan N, Pal S. Expectation maximization-based likelihood inference for flexible cure rate models with Weibull lifetimes. Stat Methods Med Res. 2016;25(4):1535–1563. doi: 10.1177/0962280213491641
  • Venzon DJ, Moolgavkar SH. A method for computing profile-likelihood-based confidence intervals. J R Stat Soc Ser C. 1988;37(1):87–94.
  • Murphy SA, Van der Vaart AW. On profile likelihood. J Am Stat Assoc. 2000;95(450):449–465. doi: 10.1080/01621459.2000.10474219
  • Fox PA, Hall AP, Schryer NL. The port mathematical subroutine library, version 3. Murray Hill (NJ): AT& T Bell Laboratories; 1997.
  • Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York: Springer; 2002.
  • Zipkin EF, Leirness JB, Kinlan BP, et al. Fitting statistical distributions to sea duck count data: implications for survey design and abundance estimation. Stat Methodol. 2014;17:67–81. doi: 10.1016/j.stamet.2012.10.002
  • Xu L, Paterson AD, Turpin W, et al. Assessment and selection of competing models for zero-inflated microbiome data. PLoS One. 2015;10(7):1–30.
  • Young DS, Raim AM, Johnson NR. Zero-inflated modelling for characterizing coverage errors of extracts from the U.S. census bureau's master address file. J R Stat Soc Ser A. 2017;180(1):73–97. doi: 10.1111/rssa.12183
  • Dulvy NK, Fowler SL, Musick JA, et al. Extinction risk and conservation of the world's sharks and rays. eLIFE. 2014;3(e00590):1–34.
  • Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90(430):773–795. doi: 10.1080/01621459.1995.10476572
  • Dunn PK, Smyth GK. Randomized quantile residuals. J Comput Graph Stat. 1996;5:236–244.
  • Schmidt M, Hurling R. A spatially-explicit count data regression for modeling the density of forest cockchafer (Melolontha hippocastani) larvae in the Hessian Reid (Germany). Forest Ecosyst. 2014;1(19):1–19.
  • Machado JAF, Santos Silva JMC. Quantiles for counts. J Am Stat Assoc. 2005;100(472):1226–1237. doi: 10.1198/016214505000000330
  • Erhardt V. Zero inflated generalized poisson (zigp) regression models; 2010. R package version 3.8; Available from: https://cran.r-project.org/web/packages/ZIGP/index.html.
  • Espinoza M, Cappo M, Heupel MR, et al. Quantifying shark distribution patterns and species-habitat associations: implications of marine park zoning. PLoS One. 2014;9(9):1–17. doi: 10.1371/journal.pone.0106885
  • Zuur AF, Ieno EN. Beginner's guide to zero-inflated models with R. Newburgh, United Kingdom: Highland Statistics Ltd.; 2016.
  • Gill J, King G. What to do when your hessian is not invertible – alternatives to model respecification in nonlinear estimation. Sociol Methods Res. 2004;33(1):54–87. doi: 10.1177/0049124103262681
  • Gilbert P, Varadhan R. Numderiv: accurate numerical derivatives; 2016. R package version 2016.8-1; Available from: https://CRAN.R-project.org/package=numDeriv.
  • Sellers KF, Shmueli G. Data dispersion: now you see it… now you don't. Commun Stat Theory Methods. 2013;42:1–14. doi: 10.1080/03610926.2011.621575

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