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...
and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. 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 ...
There are many books on linear and generalised linear models (GLMs). Some treat the linear model as a special case of the GLM, others treat the GLM as a generalisation of the linear model. The present book has chosen the latter option, which I think is more natural. One advantage of th...
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...
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 ...
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 ...
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 ...
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...
This paper describes how to establish exact formulas for calculating ranks and inertias of covariances of predictors and estimators of parameter spaces in general linear models (GLMs), and how to use the formulas in statistical analysis of GLMs. We first derive analytical expressions of best linear...
Permutation methods are commonly used to test significance of regressors of interest in general linear models (GLMs) for functional (image) data sets, in particular for neuroimaging applications as they rely on mild assumptions. Permutation inference for GLMs typically consists of three parts: choosing...