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Regression Analysis of Count Data A. Colin Cameron
Publisher: Cambridge University Press

To address this so-called overdispersion problem, it has been proposed to model count data with negative binomial (NB) distributions [9], and this approach is used in the edgeR package for analysis of SAGE and RNA-Seq [8,10]. For our analysis, we counted a signal as an early alarm if its fell within a 2-week window preceding the signal in the CDC data, so long as it was not a continuation of a previous alarm. While Poisson regression is often used as a baseline model for count data, its assumption of equi-dispersion is too restrictive for many empirical applications. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package. New York: Cambridge University Press; 1998. Cameron AC, Trivedi PK: Regression Analysis of Count Data. Regression Analysis of Count Data (Econometric Society Monographs) Regression Analysis of Count Data (Econometric Society Monographs). A suitable error model are required. Regression Analysis of Count Data. Measurement of malaria response to fluctuations in climate variables offers a way to address these New York: Oxford Science; 1991. Models of the impact of climate change on the global malaria burden now have access to high-resolution climate data, but malaria surveillance data tends to be less precise, making model calibration problematic.