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  • 标题:Mean and Variance Modeling of Under- and Overdispersed Count Data
  • 本地全文:下载
  • 作者:David M. Smith ; Malcolm J. Faddy
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2016
  • 卷号:69
  • 期号:1
  • 页码:1-23
  • 语种:English
  • 出版社:University of California, Los Angeles
  • 摘要:This article describes the R package CountsEPPM and its use in determining maximum likelihood estimates of the parameters of extended Poisson process models. These provide a Poisson process based family of flexible models that can handle both underdispersion and overdispersion in observed count data, with the negative binomial and Poisson distributions being special cases. Within CountsEPPM models with mean and variance related to covariates are constructed to match a generalized linear model formulation. Use of the package is illustrated by application to several published datasets.
  • 关键词:Poisson distribution;underdispersion;overdispersion;negative binomial distribution;extended Poisson process models
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