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

the 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...

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

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

the vector of increments, Δ β {\displaystyle \Delta {\boldsymbol {\beta }}} is known as the shift vector. At each iteration the model is linearized by...

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

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

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...

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

replacing the vector β of the classical linear regression model. Multivariate analogues of ordinary least squares (OLS) and generalized least squares...

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

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

sampling variance within the class of linear unbiased estimators, if the errors in the linear regression model are uncorrelated, have equal variances...

logit model and numerous other methods, models, algorithms, etc. with the same basic setup (the perceptron algorithm, support vector machines, linear discriminant...

generalized to discrete variables with more than two possible values.) Linear errors-in-variables models were studied first, probably because linear models...

by Vector Space Methods. New York: John Wiley & Sons. pp. 78–102. ISBN 978-0-471-18117-0. Rao, C. R.; Toutenburg, H.; et al. (2008). Linear Models: Least...

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...

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...

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

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...

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...

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

the model effects are random variables. It is a kind of hierarchical linear model, which assumes that the data being analysed are drawn from a hierarchy...

variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables...

mathematics, a generalized flag variety (or simply flag variety) is a homogeneous space whose points are flags in a finite-dimensional vector space V over...

qualify Euclidean vectors as an example of the more generalized concept of vectors defined simply as elements of a vector space. Vectors play an important...

Fraction of variance unexplained Function approximation Generalized linear model Kriging (a linear least squares estimation algorithm) Local regression Modifiable...

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

least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one[clarification...