Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?

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Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? is …
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scholarly articleQ13442814

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P356DOI10.1890/07-0043.1
P698PubMed publication ID18051645
P5875ResearchGate publication ID5795731

P50authorJay M Ver HoefQ56768632
P2093author name stringPeter L Boveng
P2860cites workEstimating the Dimension of a ModelQ29542197
Spatial modelling of individual-level parasite counts using the negative binomial distribution.Q38868242
Occupancy-abundance models for predicting densities of three leaf beetles damaging the multipurpose tree Sesbania sesban in eastern and southern AfricaQ39462449
Modeling by shortest data descriptionQ55966897
Comparing species abundance modelsQ57062713
Species richness estimators: how many species can dance on the head of a pin?Q57266226
A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, AlaskaQ58055949
Zero-Inflated Poisson Regression, with an Application to Defects in ManufacturingQ59109625
P433issue11
P407language of work or nameEnglishQ1860
P1104number of pages7
P304page(s)2766-2772
P577publication date2007-11-01
P1433published inEcologyQ1013420
P1476titleQuasi-Poisson vs. negative binomial regression: how should we model overdispersed count data?
P478volume88

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