Generalized Linear Models and Extensions

By Russell B. Millar. 2011

1,144 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3fa4e2168a50aaf88aa0cb9b752a0582d69d1552

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Citations in the corpus

Compositional Matrix-Space Models for Sentiment Analysis (2011, background): “…for posterior probabilities for all values of k : this is its derivative: Therefore the negative loglikelihood of the training data will look like the following (Hardin and Hilbe, 2007): where r is the number of ordinal classes, ξ i is the score of i -th phrase, I is the indicator…”
Fitting concentration and load rating curves with generalized linear models (2008, methodology): “Hardin and Hilbe, 2001). The correlation is necessarily sensitive to outliers. Note, however, that while standard regression necessarily maximizes its definition of R(2), given a particular dataset and model, this is not automatically the case with GLMs and this definition of R(2). Third, a standard”
Desirability bias in foresight: Consequences for decision quality based on Delphi results (2011, methodology): “In all of our regression analyses, we accounted for the correlations and the clustered structure by using Generalized Linear Models (GLMs) with fixed effects and the modified sandwich estimator of variance for clustered data [17,18,68,69].”
Modeling zero-inflated count data with glmmTMB (2017, background): “For family=nbinom1, the variance increases linearly with the mean as σ2 = μ(1 + α), with α > 0; for family=nbinom2, the variance increases quadratically with the mean as σ2 = μ(1 + μ/θ), with θ > 0 (Hardin & Hilbe, 2007).”
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling (2017, background): “For family = nbinom1 , the variance increases linearly with the mean as σ 2 = µ ( 1 + α ) , with α > 0; for family = nbinom2 , the variance increases quadratically with the mean as σ 2 = µ ( 1 + µ / θ ) , with θ > 0 (Hardin and Hilbe, 2007).”
Exposure to Interparental Violence and Childhood Physical and Emotional Abuse as Related to Physical Aggression in Undergraduate Dating Relationships (2010, methodology): “Zero-inflated Poisson (ZIP) regression appropriately models count outcomes with a large proportion of zeros (Atkins and Gallop 2007; Hardin and Hilbe 2001).”
Adoption of improved maize variety among farm households in the northern region of Ghana (2017, neutral): “Hence, relative risk ratios are used ∗ ji = Xi j + ij (1) P(Yi = k∕Xi) = Zik K∑ j=1 Zij = Xi k K∑ j=1 Xi j Page 6 of 14 to explain the alternatives (Borooah, 2002; Guris, Metin, & Caglayan, 2006; Hardin & Hilbe, 2001).”
Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps (2012, methodology): “We employ the Akaike information criteria to choose across the different distributions and the other two measures to choose among link functions (Hardin & Hilbe, 2007).”
From Minds to Markets (2012, methodology): “The negative binomial model generalizes the Poisson model by introducing an individual unobserved disturbance term that allows the mean and variance to vary (Hardin & Hilbe, 2007).”
What Teachers Think about Self-Regulated Learning: Investigating Teacher Beliefs and Teacher Behavior of Enhancing Students’ Self-Regulation (2012, neutral): “logistic regressions [45] since our dependent variable was”
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction (2015, positive): “Problems of this form include logistic regression, support vector machines, lasso, and virtually all generalized linear models (Hardin & Hilbe, 2001).”
A case study of hurdle and generalized additive models in astronomy: the escape of ionizing radiation (2018, background): “The details of these algorithms can be found in Hardin & Hilbe (2012).”