In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing...

In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random...

hierarchical generalized linear models extend generalized linear models by relaxing the assumption that error components are independent. This allows models to...

class of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models (GLMs). In particular...

Nelder, J. A. (January 1, 1983). "An outline of generalized linear models". Generalized Linear Models. Springer US. pp. 21–47. doi:10.1007/978-1-4899-3242-6_2...

In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear response variable depends linearly on unknown smooth...

The generalized functional linear model (GFLM) is an extension of the generalized linear model (GLM) that allows one to regress univariate responses of...

"linear model" is not usually applied. One example of this is nonlinear dimensionality reduction. General linear model Generalized linear model Linear...

regression using similar techniques. When viewed in the generalized linear model framework, the probit model employs a probit link function. It is most often...

In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory...

statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. It based on the generalized linear model with the...

discuss mainly linear mixed-effects models rather than generalized linear mixed models or nonlinear mixed-effects models. Linear mixed models (LMMs) are statistical...

regression for contingency tables, a type of generalized linear model. The specific applications of log-linear models are where the output quantity lies in the...

These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These...

In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...

probabilities less than zero or greater than one. Generalized linear model § Binary data Fractional model For a detailed example, refer to: Tetsuo Yai, Seiji...

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

Google books Dawes, Robyn M. (1979). "The robust beauty of improper linear models in decision making". American Psychologist, volume 34, pages 571-582...

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...

in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least...

In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression...

negatively. Mathematics portal Non-linear least squares Curve fitting Generalized linear model Local regression Response modeling methodology Genetic programming...

In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there...

specialization of generalized least squares, when all the off-diagonal entries of the covariance matrix of the errors, are null. The fit of a model to a data...

Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters...

Fisher information), the least-squares method may be used to fit a generalized linear model. The least-squares method was officially discovered and published...

|}^{2}.} IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating...

Models. New York: Cambridge University Press. pp. 119–124. ISBN 978-0-521-68689-1. Hardin, James; Hilbe, Joseph (2007). Generalized Linear Models and...

Poisson model] is true, but simply use it as a device for deriving the likelihood." McCullagh and Nelder's book on generalized linear models has a chapter...

to other statistical models including generalized linear models, generalized estimating equations, proportional hazards models, and M-estimators. Lasso's...