Modeling Count Data
By Joseph Hilbe. International Encyclopedia of Statistical Science, 2014
650 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f645c4f753a163940594e0e2118220f209f4d24b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Rüdiger Mutz
- Robin Haunschild
- Lutz Bornmann
- Joel Gehman
- Matthew G. Grimes
- S. Ceci
- Mike W.-L. Cheung
- Salvatore Ruggieri
Citations in the corpus
- Hidden Badge of Honor: How Contextual Distinctiveness Affects Category Promotion among Certified B Corporations (2017, methodology): “Hurdle models are now considered one of the foremost methods for dealing with such data (Cameron & Trivedi, 2013; Hilbe, 2014).”
- Effects of spatial autocorrelation and imperfect detection on species distribution models (2018, neutral): “Similarly, the typical model for abundance comes in form of a Poisson regression (Poisson GLM) or one of its variants (Cameron & Trivedi, 1998; Hilbe, 2014).”
- Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases (2020, background): “bution of residuals is assumed for each segment. Publication counts is a count variable. The variable includes positive integer values with zero. This implies that the values are Poisson distributed (Hilbe, 2014, p. 2). In this study, however, a logarithmic transformation (base e) of the publication”