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Selecting the Best Linear Mixed Model Under REML

https://doi.org/10.1198/000313006x90396
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf02294361openalex-snapshot
Model Selection and Akaike's Information Criterion (AIC): The General Theory and its Analytical Extensions
inferredcites →https://doi.org/10.1016/j.csda.2006.05.018openalex-snapshot
Testing transformations for the linear mixed model
inferredcites →https://doi.org/10.1080/03610919308813143openalex-snapshot
Covariance structure selection in general mixed models
inferredcites →https://doi.org/10.1080/03610929708831934openalex-snapshot
Regression and time series model selection using variants of the schwarz information criterion
inferredcites →https://doi.org/10.1093/biomet/61.2.383openalex-snapshot
Bayesian Inference for Variance Components Using Only Error Contrasts
inferredcites →https://doi.org/10.1093/biomet/76.2.297openalex-snapshot
Regression and Time Series Model Selection in Small Samples
inferredcites →https://doi.org/10.1109/tac.1974.1100705openalex-snapshot
A new look at the statistical model identification
inferredcites →https://doi.org/10.1111/1467-9868.00092openalex-snapshot
Likelihood Ratio Tests for Fixed Model Terms using Residual Maximum Likelihood
inferredcites →https://doi.org/10.1111/1467-9868.00335openalex-snapshot
Regression Model Selection—A Residual Likelihood Approach
inferredcites →https://doi.org/10.1142/9789812385451openalex-snapshot
Regression and Time Series Model Selection
inferredcites →https://doi.org/10.1201/9781482293272openalex-snapshot
Linear and Nonlinear Models for the Analysis of Repeated Measurements
inferredcites →https://doi.org/10.1214/aos/1176344136openalex-snapshot
Estimating the Dimension of a Model
inferredcites →https://doi.org/10.2307/2291091openalex-snapshot
Bayes Factors
inferredcites →https://doi.org/10.2307/3802723openalex-snapshot
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Fixed and random effects models
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Introduction
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Linear Model with Fixed Effects and Correlated Errors
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ARMD Trial: Modeling Correlated Errors for Visual Acuity
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Linear Mixed-Effects Model
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Data Exploration
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Linear Models with Homogeneous Variance
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Fitting Linear Models with Homogeneous Variance: The lm() and gls() Functions
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ARMD Trial: Linear Model with Homogeneous Variance
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Linear Models with Heterogeneous Variance
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Fitting Linear Models with Heterogeneous Variance: The gls() Function
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ARMD Trial: Linear Model with Heterogeneous Variance
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Association of Lockdown Policies with COVID-19 Early Case Growth Rates in the United States
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varTestnlme: An R Package for Variance Components Testing in Linear and Nonlinear Mixed-Effects Models
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Sociality of Future Outcomes Moderates The Effects Of Warmth And Competence On Social Optimism Bias - A Pre-Registered Study
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Variable Selection Theorem for the Analysis of Covariance Model
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