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{{short description|Generalized linear model}} ...n Asymptotically Optimal Confidence Regions and Tests for High-Dimensional Models |journal=The Annals of Statistics |date=2014 |volume=42 |issue=3 |pages=116 ...

...er share some common features. In this situation, using generalized linear models and ignoring the correlations may cause problems.<ref name=CDA>{{cite book| ...tly [[monotone function]] of <math>u</math>. In a hierarchical generalized linear model, the assumption on <math>y|u</math> and <math>u</math> need to be mad ...

...ernatives is modeled, instead of simply outputting a single value, as in [[linear regression]]. ...of successes in 1 trial, either 0 or 1. The most common binary regression models are the [[logit model]] ([[logistic regression]]) and the [[probit model]] ...

{{About|the statistical method|additive color models|Additive color}} ...rthermore, the ''AM'' is more flexible than a [[linear regression|standard linear model]], while being more interpretable than a general regression surface a ...

...tomata; a transformation is given [[Büchi automata#Transforming from other models of description to non-deterministic B.C3.BCchi automata|here]]. ...[[Linear temporal logic to Büchi automaton|algorithms]] that translate a [[Linear temporal logic|LTL]] formula ...

...assumption between the response (scalar or functional) and the functional linear predictor is replaced by an additivity assumption. ...'''FAM'''<ref>{{cite journal|last=Müller and Yao|title=Functional Additive Models|journal=Journal of the American Statistical Association|year=2008|volume=10 ...

...Technique for improving the efficiency of estimators in conditional moment models}} ...value|expectation]] function (defining "moment conditions") and use the [[generalized method of moments]] (GMM). However, there are infinitely many moment condit ...

The '''linear-nonlinear-Poisson (LNP) cascade model''' is a simplified functional model o [[Image:LNPModelDiagram.png|right|thumb|300px|The Linear-Nonlinear-Poisson Cascade Model]] ...

In applied statistics, '''fractional models''' are, to some extent, related to [[binary response model]]s. However, ins ...The first approach uses a [[log-odds]] transformation of {{mvar|y}} as a linear function of {{mvar|x_i}}, i.e., <math>\operatorname{logit} y = \l ...

'''Generalized regression neural network (GRNN)''' is a variation to [[radial basis functi ...works.com/help/nnet/ug/generalized-regression-neural-networks.html | title=Generalized Regression Neural Networks - MATLAB & Simulink - MathWorks Australia}}</ref ...

...rlain]]'s approach to unobserved effects models is a way of estimating the linear unobserved effects, under fixed effect (rather than [[random effects]]) ass ...ed effect ''c_i'', Chamberlain proposed to replace it with the [[linear projection]] of it onto the explanatory variables in all time periods. Spec ...

...with the statistical analysis, say <math> \mu </math>, for which competing models lead to different estimates, say <math> \hat\mu_j </math> for model <math> ...the mean value. Secondly, the FIC formulae depend on the specifics of the models used for the observed data and also on how precision is to be measured. The ...

...hers [[projection pursuit]], [[generalized linear model|generalized linear models]], and as [[activation function|activation functions]] in [[neural network| ...

...tional [[additive model]]s are three special cases of functional nonlinear models. == Functional linear models (FLMs) == ...

...ame=":0" /> '''dynamic expectation maximization'''<ref name=":1" /> and '''generalized predictive coding'''. '''Definition''': Generalized filtering rests on the [[tuple]] <math>(\Omega,U,X,S,p,q)</math>: ...

...te journal|last=Muller and Stadtmuller|title=Generalized Functional Linear Models|journal=The Annals of Statistics|year=2005|volume=33|issue=2|pages=774–805| ...ce the dimension of the predictor process has been reduced, the simplified linear predictor allows to use GLM and [[quasi-likelihood]] estimation techniques ...

...ref> is a modification of the [[Einstein–Hilbert action]] to include the [[generalized Gauss–Bonnet theorem|Gauss–Bonnet term]]<ref>{{Cite book|last=Roos|first=Ma ...a topological [[divergence theorem|surface term]]. This follows from the [[generalized Gauss–Bonnet theorem]] on a 4D manifold ...

...ical classification|classification]].<ref>{{cite journal |title=Regression Models with Ordinal Variables |first1=Christopher |last1=Winship |first2=Robert D. ==Linear models for ordinal regression== ...

{{short description|Specific multivariate linear model}} ...atistics]] is a specific multivariate linear model, also known as GMANOVA (Generalized Multivariate Analysis-Of-Variance).<ref>{{cite book ...

The '''generalized additive model for location, scale and shape''' ('''GAMLSS''') is a [[semip ...ale (e.g., variance) and shape (skewness and kurtosis) – can be modeled as linear, nonlinear or smooth functions of explanatory variables. ...