Live briefing
Everything about General linear model
Statistical linear model
The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is not a separate statistical linear model. The various multiple linear regression models may be compactly written as Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: General 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: Vector generalized linear modelA closely related topic
- Go sideways: Multiple linear regressionA 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
A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model
Linear parameter-varying model predictive control for nonlinear systems using general polytopic tubes
Improved cerebrovascular reactivity mapping using coherence weighted general linear model in the frequency domain
📚 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
- 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…
- Multiple linear regression Multiple linear regression, also known as multivariable linear regression, is a generalization of simple linear regression to the case of more than one…
- 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…
- Multinomial logistic regression In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than…
- Nonlinear regression In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination…
- Principal component regression In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). PCR is a form…
- 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…
- Gauss–Markov theorem In statistics, the Gauss–Markov theorem (or simply Gauss theorem for some authors) states that, under certain conditions, the ordinary least squares (OLS)…