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Using N2pc variability to probe functionality: Linear mixed modelling of trial EEG and behaviour
Clayton Hickey +3 more
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Improvement of mixed predictors in linear mixed models
Journal of Applied Statistics, 2020In this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictor to the Liu and mixed predictors are done in the sense of mean ...
Özge Kuran, M. Revan Özkale
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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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Linear and Generalized Linear Mixed Models and Their Applications
Technometrics, 2008(2008). Linear and Generalized Linear Mixed Models and Their Applications. Technometrics: Vol. 50, No. 1, pp. 93-94.
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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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