Regression Analysis of Count Data
By W. Holmes, William C. Rinaman. 2014
492 citations recorded by the source corpus.
Subfields: Demography, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6222d1603e8d92d7633ec56bc1ebe57f4dd55704
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In Demography
In Statistics and Probability
- Amanda Birmingham
- Yoshiki Vazquez-Baeza
- Alejandro Reyes
- Antonio Gonzalez
- Wolfgang Huber
- Adam Tatarynowicz
- Simon Anders
- Kyle Bittinger
Citations in the corpus
- Developing a Liu estimator for the negative binomial regression model: method and application (2013, unknown): “Furthermore, the values of δ equal to 0.5 and 1 which approximately corresponds to the values obtained in the empirical applications in Green [9] and Cameron and Trivedi [10], respectively.”
- Normalization and microbial differential abundance strategies depend upon data characteristics (2017, methodology): “These parametric models are composed of a generalized linear model (GLM) that assumes a distribution [46], and the choice of distribution is often debatable [4, 18, 45, 47–53].”
- Detecting differential usage of exons from RNA-seq data (2012, methodology): “(2010), who used Drosophila melanogaster cell lines and studied the effect of knocking down pasilla with RNA-Seq. The gene pasilla and its mammalian homologues NOVA1 and NOVA2 are well-studied splicing factors. Brooks et al. (2010) prepared libraries from RNA extracted from seven biologically indepe”
- Exploring the Locus of Invention: The Dynamics of Network Communities and Firms' Invention Productivity (2014, background): “what extent a firm is positioned closer to the core than to the periphery of its network community, is more precise than the discrete model of core/periphery (for a similar approach, see Cattani and Ferriani [2008]).”