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2018
We have covered a variety of statistical models in this book, but all have shared a common feature: The criterion and error term were treated as random variables, but all of the predictors were assumed to be fixed. In this chapter, we will consider models that include a broader mixture of fixed and random variables.
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We have covered a variety of statistical models in this book, but all have shared a common feature: The criterion and error term were treated as random variables, but all of the predictors were assumed to be fixed. In this chapter, we will consider models that include a broader mixture of fixed and random variables.
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1989
In most design set-ups such as block designs or row-column designs classification effects such as block effects or row (column) effects are regarded fixed. When these are also considered random variables, we have one or more additional sources of information for estimating treatment effect parameters. Such models are known as mixed effects models.
Kirti R. Shah, Bikas K. Sinha
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In most design set-ups such as block designs or row-column designs classification effects such as block effects or row (column) effects are regarded fixed. When these are also considered random variables, we have one or more additional sources of information for estimating treatment effect parameters. Such models are known as mixed effects models.
Kirti R. Shah, Bikas K. Sinha
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2012
With one exception the models that we have treated before this chapter contain a single source of variability. In the linear models with Gaussian error, the variance of the population was estimated from the residuals. In the GLM models used to describe the judgments of observers, the variability of observer’s choices is given by that of the binomial ...
Kenneth Knoblauch, Laurence T. Maloney
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With one exception the models that we have treated before this chapter contain a single source of variability. In the linear models with Gaussian error, the variance of the population was estimated from the residuals. In the GLM models used to describe the judgments of observers, the variability of observer’s choices is given by that of the binomial ...
Kenneth Knoblauch, Laurence T. Maloney
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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
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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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Modeling the effect of mixing in biodiesel production
Bioresource Technology, 2011The transesterification reaction models available in the literature are valid only for one particular mixing condition. In this work, a modeling strategy is presented in order to predict the effect of mixing conditions in the transesterification process.
Ana S R, Brásio +3 more
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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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