Generalized Linear Models

By P. McCullagh, John A. Nelder. 1984

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Citations in the corpus

Hierarchical Mixtures of Experts and the EM Algorithm (1993, methodology): “These models are smoothed piecewise analogs of the corresponding GLIM models (cf. McCullagh & Nelder, 1983).”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, methodology): “i . Using the inverse-propensity as a single additional covariate is sufficient to achieve double robustness, because the estimate then becomes the solution to an AIPW estimating equation ( Bang and Robins, 2005 ).”
An Analysis of the New York City Police Department's “Stop-and-Frisk” Policy in the Context of Claims of Racial Bias (2007, methodology): “In classical generalized linear modeling or generalized estimating equations, overdispersion can be estimated using a chisquared statistic, with standard errors inflated by the square root of the estimated overdispersion ( McCullagh and Nelder 1989 ).”
From pedotransfer functions to soil inference systems (2002, methodology): “Other modern numerical and statistical methods can also be applied such as Generalised Linear Models (McCullagh and Nelder, 1989), General Additive Models (GAM, Hastie, 1992), the Group Method of Data Handling (GMDH, Farrow, 1984; Pachepsky and Rawls, 1999), Multiple Adaptive Regression Splines (MAR”
An Experiment on Public Speaking Anxiety in Response to Three Different Types of Virtual Audience (2002, methodology): “The MPRCS and Somatic variables were treated as the sum of binary responses, and thus analysed with standard binomial logistic regression (McCullagh and Nelder, 1983).”
Oversampling for Imbalanced Learning Based on K-Means and SMOTE (2017, methodology): “Fitting the model is an optimization problem which can be solved using simple optimizers which require no hyperparameters to be set [35] .”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, methodology): “logistic curve (McCullough and Nelder 1989).”