Negative Binomial Regression: Preface

By Joseph Hilbe. 2007

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Citations in the corpus

Effective Bayesian Modeling of Groups of Related Count Time Series (2014, background): “The negative binomial distribution (parametrized by the mean µ instead of the more usual probability of success in a trial; e.g. Hilbe 2011) is given by PNB(y | µ, α) = ( α+ y − 1 α− 1 )( α µ+ α )α( µ µ+ α )y , (1) where ( n m ) ≡ Γ(n+1)Γ(m+1)Γ(n−m+1) is the binomial coefficient.”
Nesting Density and Reproductive Success of Piping Plovers in Response to Storm- and Human-Created Habitat Changes (2009, methodology): “For example, we used the negative binomial distribution for count data such as arthropod abundance (Hilbe 2007).”
What drives innovation output from subsidized R&D cooperation?—Project-level evidence from Germany (2012, methodology): “Since Pearson dispersion statistics (Hilbe, 2007) indicate significant overdispersion of these variables we apply negative binomial regression as the estimation technique (Cameron and Trivedi, 2005; Hilbe, 2007).”
Functional Form and Heterogeneity in Models for Count Data (2007, methodology): “[See, as well, Hilbe (2007).]”
Constant Step Size Stochastic Gradient Descent for Probabilistic Modeling (2018, neutral): “Poisson regression may be appropriate when the dependent variable is a count, for example in genomics, network packet analysis, crime rate analysis, ﬂu-orescence microscopy, etc. (Hilbe, 2011).”
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (2008, methodology): “Data Analysis Main effects of covariates (age, sex, household size, and country) on numbers of contacts were assessed using multiple censored negative binomial regression [27].”
A dimensional analysis of creativity and mental illness: Do anxiety and depression symptoms predict creative cognition, creative accomplishments, and creative self-concepts? (2010, methodology): “Negative binomial models can model count processes for overdispersed outcomes (Hilbe, 2007; Long, 1997), so they are useful models for analyzing the CAQ domain scores.”
LibRA (2019, positive): “Therefore, instead of using purely linear regression models, we build negative binomial regression models [67], ones that essentially regress the logarithm of the dependent variables with the independent variables [67].”
A protocol for conducting and presenting results of regression‐type analyses (2016, background): “It is important to find the source of the overdispersion and adjust the model accordingly (Hilbe 2011), or the © 2016 The Authors.”
Modeling Count Data (2014, background): “When Poisson overdispersion is real, and not merely apparent (Hilbe, 2007), a count model other than Poisson is required.”
Which factors help authors produce the highest impact research? Collaboration, journal and document properties (2013, methodology): “As the results from the logit model are easier to interpret, it was used (Hilbe, 2011).”