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2019
The use of linear mixed-effect models is becoming increasingly popular in neuroimaging. These models extend the simple linear models by allowing both fixed and random effects, and are particularly useful for clustered and hierarchical data. These are the materials from a session held at the MRC Cognition and Brain Sciences Unit, Cambridge, UK, where ...
Roni Tibon +2 more
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The use of linear mixed-effect models is becoming increasingly popular in neuroimaging. These models extend the simple linear models by allowing both fixed and random effects, and are particularly useful for clustered and hierarchical data. These are the materials from a session held at the MRC Cognition and Brain Sciences Unit, Cambridge, UK, where ...
Roni Tibon +2 more
openaire +1 more source
2022
This chapter describes mixed-effect models, which are a class of statistical tests that build on simpler tests, such as regression, t-tests, and ANOVA. It explains that mixed-effects models involve the inclusion of one or more random effects. The general idea of mixed effects models is to try to account for as much of the overall variance in the data ...
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This chapter describes mixed-effect models, which are a class of statistical tests that build on simpler tests, such as regression, t-tests, and ANOVA. It explains that mixed-effects models involve the inclusion of one or more random effects. The general idea of mixed effects models is to try to account for as much of the overall variance in the data ...
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A Mixed-Effects Model for Categorical Data
Biometrics, 1985A mixed model for categorical data from unbalanced designs which is directly analogous to a two-way ANOVA model for quantitative data is proposed. An extension of the fitting constants method is developed to estimate model variance components based on appropriate reductions in sums of squares.
P J, Beitler, J R, Landis
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The Mixed Effects Trend Vector Model
Multivariate Behavioral Research, 2012Maximum likelihood estimation of mixed effect baseline category logit models for multinomial longitudinal data can be prohibitive due to the integral dimension of the random effects distribution. We propose to use multidimensional unfolding methodology to reduce the dimensionality of the problem.
Mark, de Rooij, Martijn, Schouteden
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Mixed-Effects Modeling of Optimisation Algorithm Performance [PDF]
, 2009 The learning curves of optimisation algorithms, plotting the evolution of the objective vs. runtime spent. can be viewed as a sample of longitudinal data. In this paper we describe mixed-effects modeling, a standard technique in longitudinal data analysis, and give an example of its application to algorithm performance modeling.
Matteo Gagliolo +2 more
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Testing in mixed-effects FANOVA models
Journal of Statistical Planning and Inference, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Abramovich F, Angelini C
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An Estimation Method for the Semiparametric Mixed Effects Model
Biometrics, 1999Summary.A semiparametric mixed effects regression model is proposed for the analysis of clustered or longitudinal data with continuous, ordinal, or binary outcome. The common assumption of Gaussian random effects is relaxed by using a predictive recursion method (Newton and Zhang, 1999) to provide a nonparametric smooth density estimate. A new strategy
Tao, Huageng +3 more
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