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Cook’s distance for generalized linear mixed models

Computational Statistics & Data Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Luis Gustavo B. Pinho, Juvêncio Santos Nobre, Julio M. Singer   +2 more
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Linear and generalized linear mixed models

2015
AbstractGeneralized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random ...
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Generalized Linear Mixed Models

2017
For analyzing repeated measures data, the necessity of considering the relationships between outcome variables as well as between outcome variables and explanatory variable are of concern. We have discussed about such models in previous chapters. All the models proposed in various chapters are fixed effect models. However, in some cases, the dependence
M. Ataharul Islam, Rafiqul I. Chowdhury
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General Linear and Mixed Models

Abstract This chapter provided an overview of general linear models (GLM). We examine both OLS and GLS estimators. We then extend this to the general mixed model.
Bruce Walsh, Peter M. Visscher, Michael Lynch   +2 more
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An alternative specification of generalized linear mixed models

Computational Statistics & Data Analysis, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sartori N., Severini T. A., Marras E.
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Full Credibility with Generalized Linear and Mixed Models

ASTIN Bulletin, 2009
AbstractGeneralized linear models (GLMs) are gaining popularity as a statistical analysis method for insurance data. For segmented portfolios, as in car insurance, the question of credibility arises naturally; how many observations are needed in a risk class before the GLM estimators can be considered credible?
Garrido, José, Zhou, Jun
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Generalized, Linear, and Mixed Models

Technometrics, 2003
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Generalized Multi-linear Mixed Effects Model

2016
Recently, many applications tend to find common and distinctive features from a group of datasets, of which distributions and structures are generally various. However, most existing methods can just cope with specific problems with fixed distributions and structures.
Chao Li 0013, Lili Guo, Zheng Dou, Guangzhen Si, Chunmei Li   +4 more
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Linear and generalized linear mixed effects models

2009
In Chapter 8 we learned about the concept of hierarchical modeling, a data analysis approach that is appropriate when we have multiple measurements within each of several groups. In that chapter, variation in the data was represented with a between-group sampling model for group-specific means, in addition to a within-group sampling model to represent ...
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Using Generalized Linear (Mixed) Models in HCI

2016
In HCI we often encounter dependent variables which are not (conditionally) normally distributed: we measure response-times, mouse-clicks, or the number of dialog steps it took a user to complete a task. Furthermore, we often encounter nested or grouped data; users are grouped within companies or institutes, or we obtain multiple observations within ...
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