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pro vyhledávání: '"Kimberly S. Weems"'
Publikováno v:
Communications in Statistics - Theory and Methods. 52:4692-4718
The bivariate Poisson distribution is a natural choice for modeling bivariate count data. Its constraining assumption, however, limits model flexibility in some contexts. This work considers the tr...
Publikováno v:
Teaching and Learning Mathematics Online ISBN: 9781351245586
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::544f94ba8ebdebe5d6f6a7e8780bb1f7
https://doi.org/10.1201/9781351245586-8
https://doi.org/10.1201/9781351245586-8
Autor:
Kimberly S. Weems, Paul J. Smith
Publikováno v:
Metrika. 81:985-1004
The generalized linear mixed model (GLMM) extends classical regression analysis to non-normal, correlated response data. Because inference for GLMMs can be computationally difficult, simplifying distributional assumptions are often made. We focus on
Publikováno v:
Journal of Statistical Distributions and Applications, Vol 4, Iss 1, Pp 1-1 (2017)
Publikováno v:
Journal of Statistical Distributions and Applications, Vol 4, Iss 1, Pp 1-21 (2017)
The Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the Conway-Maxwell-Poisson (CMP) distribution – a two-parameter generalization of the Poisson distribution that can accommodate data over-