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Testing transformations for the linear mixed model
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Matthew J. Gurka +2 more
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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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2013
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
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The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
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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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2009
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
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In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
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Linear Mixed Models for Longitudinal Data
Technometrics, 2001(2001). Linear Mixed Models for Longitudinal Data. Technometrics: Vol. 43, No. 3, pp. 375-375.
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Mixed Linear Model with Uncertain Paternity
Applied Statistics, 1992Summary: In animal breeding applications, mixed linear models are often used to estimate genetic parameters and to predict the breeding value of sires, under the assumption that paternity can be attributed without error. This paper considers a mixed linear model for situations in which paternity is uncertain.
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Linear and generalized linear mixed models
2015AbstractGeneralized 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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On Inverse Prediction in Mixed Linear Models
Communications in Statistics - Simulation and Computation, 2014Given training data, a model relating a multivariate response y to x, and y* from a mystery specimen, the objective is to infer what values x* might have given rise to y*. Two approaches are investigated and illustrated here. In one, inverse prediction, tenable values of x* are those at which y* does not test as an outlier.
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