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Spatial Simulation and Estimation of Generalized Linear Mixed Models with Non-Normal Data
, 2014 Lynette M. Smith +2 more
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2014
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
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Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
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2020
This chapter introduces linear mixed models, which have wide applicability in small area estimation due to their flexibility to combining different types of information and explaining sources of errors. Three of the most used fitting methods are presented under two parametrizations.
Domingo Morales +3 more
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This chapter introduces linear mixed models, which have wide applicability in small area estimation due to their flexibility to combining different types of information and explaining sources of errors. Three of the most used fitting methods are presented under two parametrizations.
Domingo Morales +3 more
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2007
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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2001
Observations often fall into groups or clusters. For example, longitudinal data consist of repeated observations on the same subjects. Hierarchical data sets typically consist of subjects nested in higher level units, such as families or GP practices.
Brian Everitt, Sophia Rabe-Hesketh
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Observations often fall into groups or clusters. For example, longitudinal data consist of repeated observations on the same subjects. Hierarchical data sets typically consist of subjects nested in higher level units, such as families or GP practices.
Brian Everitt, Sophia Rabe-Hesketh
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2012
In current clinical research repeated measures in a single subject are common. The problem with repeated measures is, that they are more close to one another than unrepeated measures. If this is not taken into account, then data analysis will lose power.
Ton J. Cleophas, Aeilko H. Zwinderman
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In current clinical research repeated measures in a single subject are common. The problem with repeated measures is, that they are more close to one another than unrepeated measures. If this is not taken into account, then data analysis will lose power.
Ton J. Cleophas, Aeilko H. Zwinderman
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Local Influence in Linear Mixed Models
Biometrics, 1998The linear mixed model has become an important tool in modelling, partially due to the introduction of the SAS procedure MIXED, which made the method widely available to practising statisticians. Its growing popularity calls for data-analytic methods to check the underlying assumptions and robustness. Here, the problem of detecting influential subjects
Lesaffre, Emmanuel, Verbeke, Geert
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2012
Fixed effects and random effects models are introduced with examples. Maximum likelihood (ML), restricted maximum likelihood (REML) and ANOVA methods of estimation of variance components are described, illustrating with the examples of one-way and two-way classification.
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Fixed effects and random effects models are introduced with examples. Maximum likelihood (ML), restricted maximum likelihood (REML) and ANOVA methods of estimation of variance components are described, illustrating with the examples of one-way and two-way classification.
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Model comparison of generalized linear mixed models
Statistics in Medicine, 2005AbstractGeneralized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing.
Xin-Yuan, Song, Sik-Yum, Lee
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