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Everything about Generalized linear model
Class of statistical models
In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Generalized linear modelA 5-minute orientation
- Skim a recent paper: Elastic Net Regularization Paths for All Generalized Linear ModelsInfluential work since 2022
- Pick up a classic book: A first course in linear model theoryThe most-reprinted book on the subject
- Read the landmark paper: Regularization Paths for Generalized Linear Models via Coordinate DescentCited 15,367 times
- Go sideways: Binomial regressionA closely related topic
- Go sideways: Vector generalized linear modelA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Regularization Paths for Generalized Linear Models via Coordinate Descent
Longitudinal data analysis using generalized linear models
glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling
Approximate Inference in Generalized Linear Mixed Models
A general and simple method for obtaining R 2 from generalized linear mixed‐effects models
🆕 Influential since 2022
Elastic Net Regularization Paths for All Generalized Linear Models
glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models
Generalized Linear Spectral Mixing Model for Spatial–Temporal–Spectral Fusion
A Federated Generalized Linear Model for Privacy-Preserving Analysis
Generalized quasi-linear mixed-effects model
📚 Books worth reading
A first course in linear model theoryNalini Ravishanker, Dipak K. Dey · 2001
Linear Mixed ModelsBrady T. West, Kathleen B. Welch · 2006
Higher-Order Growth Curves and Mixture Modeling with MplusKandauda A. S. Wickrama, Tae Lee · 2016
Plane answers to complex questionsRonald Christensen · 1987
Generalized linear models with random effectsYoungjo Lee · 2006
Univariate and multivariate general linear modelsKevin Kim · 2006
Generalized, linear, and mixed modelsCharles E. McCulloch, Shayle R. Searle · 2003
Linear models in statisticsAlvin C. Rencher, G. Bruce Schaalje · 2000
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Binomial regression In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is…
- Vector generalized linear model In statistics, the class of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models…
- Logistic regression In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent…
- Generalized functional linear model The generalized functional linear model (GFLM) is an extension of the generalized linear model (GLM) that allows one to regress univariate responses of…
- Binary regression In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output…
- Variance function In statistics, the variance function is a smooth function that depicts the variance of a random quantity as a function of its mean. The variance function…
- Poisson regression In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression…
- Probit model In statistics, a probit model is a type of regression where the dependent variable can take only two values. The word is a portmanteau, coming from probability…