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Symbolic Formulae for Linear Mixed Models
A statistical model is a mathematical representation of an often simplif...
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The correlation structure of mixed effects models with crossed random effects in controlled experiments
The design of experiments in psychology can often be summarized to parti...
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Analysis of Multivariate Data and Repeated Measures Designs with the R Package MANOVA.RM
The numerical availability of statistical inference methods for a modern...
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Some t-tests for N-of-1 trials with serial correlation
N-of-1 trials allow inference between two treatments given to a single i...
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A Statistical Model with Qualitative Input
A statistical estimation model with qualitative input provides a mechani...
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Flexible Validity Conditions for the Multivariate Matérn Covariance in any Spatial Dimension and for any Number of Components
Flexible multivariate covariance models for spatial data are on demand. ...
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Dependence of Inferred Climate Sensitivity on the Discrepancy Model
We consider the effect of different temporal error structures on the inf...
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A Note on Implementing a Special Case of the LEAR Covariance Model in Standard Software
Repeated measures analyses require proper choice of the correlation model to ensure accurate inference and optimal efficiency. The linear exponent autoregressive (LEAR) correlation model provides a flexible two-parameter correlation structure that accommodates a variety of data types in which the correlation within-sampling unit decreases exponentially in time or space. The LEAR model subsumes three classic temporal correlation structures, namely compound symmetry, continuous-time AR(1), and MA(1), while maintaining parsimony and providing appealing statistical and computational properties. It also supplies a plausible correlation structure for power analyses across many experimental designs. However, no commonly used statistical packages provide a straightforward way to implement the model, limiting its use to those with the appropriate programming skills. Here we present a reparameterization of the LEAR model that allows easily implementing it in standard software for the special case of data with equally spaced temporal or spatial intervals.
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