Foundations of Linear and Generalized Linear Models

By Alan Agresti. 2015

457 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c0610e24de9bd9ca6613e6d69493945aa548c05b

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Citations in the corpus

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, methodology): “4 Residuals and z-scores Just as mean squared error can be computed by taking the sum of squared residuals under a Gaussian likelihood, the deviance is equal to the sum of squared deviance residuals [48].”
Simple ways to interpret effects in modeling ordinal categorical data (2018, methodology): “The example uses data from a study of mental health (Agresti, 2015, section 6.3.3).”
A Deep Gravity model for mobility flows generation (2020, methodology): “In these two cases, the gravity model can be formulated as a Generalized Linear Model with a multinomial distribution 73 .”
Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models (2020, methodology): “l, for the sake of comparison among the two techniques. We consider Logistic Regression as the benchmark model, being explainable and widely used in Credit Scoring. The technique is well described in Agresti, 2015 and it usually obtains satisfactorily results on CRM datasets. Logistic Regression pro”
Handling Skewed Data: A Comparison of Two Popular Methods (2020, background): “If a link function is convenient in the sense that the inverse-linked linear model of explanatory variables adheres to the support for the expected value for that outcome, it does not further need to stabilize variance or produce normality; this is because the fitting process maximizes the likelihoo”