An Introduction to Generalized Linear Models

By Eric R. Ziegel. Technometrics, 2002

2,720 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/84bb5dc86c25d79778ac4b8f6ca560c1e889fee9

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Citations in the corpus

Fitting Tweedie's Compound Poisson Model to Insurance Claims Data: Dispersion Modelling (2002, background): “lagh and Nelder (1989) and Dobson (2001) give thorough general introductions to”
Applied Predictive Modeling (2013, methodology): “This function is an interface to a broad set of methods called generalized linear models (Dobson 2002), which includes logistic regression.”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, background): “Fortunately, estimates of regression parameters are fairly robust against misspecification of the correlation matrix (Dobson 2002).”
The effect of plant population size on the interactions between the rare plant Gentiana cruciata and its specialized herbivore Maculinea rebeli (2001, methodology): “Effects of fixed factors (such as G. cruciata population size, presence of a M. rebeli population, number of M. rebeli eggs, stem length, year, herbivore presence in a fruit, and their interactions) were tested with the Wald test (Dobson 1990).”
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, background): “Therefore, parameter estimates b will be unstable, i.e. small changes in the data may cause large changes in b (Dobson 2002, p. 94).”
Can Rapoport's rule explain tree species richness along the Himalayan elevation gradient, Nepal? (2006, methodology): “We used generalized linear models (GLM; McCullagh & Nelder, 1989; Dobson, 1990) to examine the relationships between tree richness and elevation.”
A Survey of Dimension Reduction Techniques (2002, methodology): “Dimension reduction methods related to regression include projection pursuit regression [20, 7], generalized linear [42, 10] and additive [19] models, neural network models, and sliced inverse regression and principal hessian directions [39].”
Least squares estimation in the monotone single index model (2016, methodology): “Dobson and Barnett (2008). It assumes that the observations are n i.”
The psychometric function: I. Fitting, sampling, and goodness of fit (2001, neutral): “Statistical estimation of parameters is routine in data modeling (Dobson, 1990; McCullagh & Nelder, 1989): In the context of fitting psychometric functions, probit analysis (Finney, 1952 , 1971) and a maximum-likelihood search method described by Watson (1979) are most commonly employed.”
Analyzing Skewed Continuous Outcomes With Many Zeros: A Tutorial for Social Work and Youth Prevention Science Researchers (2018, methodology): “Therefore, researchers usually use a data transformation, such as the logarithmic or Box-Cox transformation described earlier, or fit a generalized linear model (Dobson & Barnett, 2008) that allows the residuals to follow more flexible distributional shapes (an example of the latter follows).”
LINKING DEMOGRAPHIC EFFECTS OF HABITAT FRAGMENTATION ACROSS LANDSCAPES TO CONTINENTAL SOURCE-SINK DYNAMICS (2005, methodology): “Deviance values were therefore used to select the most parsimonious model (Dobson 2002).”