general linear models (GLMsleast squares estimatesregressionstatistical packagesSummary Regression and analysis of variance (ANOVA) are probably the most frequently applied of all statistical analyses. Analysis of covariance (ANCOVA), a combination of regression and ANOVA, allows researchers to determine if...
The General Linear Model Or, What the Hell’s Going on During Estimation? What we hope to cover: • Extension of linear to multiple regression • Matrix formulation of multiple regression; residuals and parameter estimates • General and Generalised Linear Models • Overdetermined models and ...
We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control ...
In this chapter we consider the analysis of data that are not well-modeled by the linear models described in Chap.5. We continue to assume that the responses are (conditionally) independent. We describe two model classes, generalized linear models (GLMs) and what we refer to as nonlinear ...
In this thesis, a class of structure-preserving general linear methods (GLMs) is considered as an alternative choice. The research performed here includes the construction of a set of theoretical tools for analysing derivatives of B-series (a generalisation of Taylor series). These tools are then...
Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regrKim, Kevin; Timm, Neildoi:10.1198/tech.2008.s544S. E AhmedTaylor & Francis GroupTechnometricsK. Kim and N. Timm, Univariate and Multivariate General Linear Mod- els: Theory and Applications With SAS...
Generalized linear models (GLMs) are a standard way of dealing with such situations. Even in high-dimensional feature spaces GLMs can be extended to deal with such situations. Penalized inference approaches, such as the \\(\\ell _1\\) or SCAD, or extensions of least angle regression, such...
The presentation of variance explained' (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value...
The explanatory value of environmental variables on a small-scale gradient of endemic and exotic arthropod species richness was examined with generalized linear models (GLMs). In addition, the impact of both endemic and exotic species richness in the communities was assessed by entering them after ...
random effects GLMsvariance componentsThis paper describes an EM algorithm for nonparametric maximum likelihood (ML) estimation in generalized linear models with variance component structure. The algorithm provides an alternative analysis to approximate MQL and PQL analyses (McGilchrist and Aisbett, 1991, ...