An improved approximation to the precision of fixed effects from restricted maximum likelihood

By Michael G. Kenward, J. Roger. Computational Statistics & Data Analysis, 2009

443 citations recorded by the source corpus.

Subfields: Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1bbcaf2c4761d3ebfb76a4a0fec96e907f8d683c

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In Environmental Engineering

In Statistics and Probability

Citations in the corpus

Multilevel Modelling of Country Effects: A Cautionary Tale (2016, methodology): “More well known are the small-sample corrections to SEs with associated sample size adjustments that have been developed for the REML estimator (Kenward and Roger, 1997, 2009), and which are available in SAS’s PROC MIXED and R’s ASREML package.”
Design-Comparable Effect Sizes in Multiple Baseline Designs (2014, background): “Parametric bootstrapping (Efron & Tibshirani, 1998, section 6.5) and small-sample corrections for the covariance matrix of fixed effect estimates (Kenward & Roger, 1997, 2009) warrant further investigation as well, as they may provide better means of estimating the effect size variance and…”
Robustness of linear mixed‐effects models to violations of distributional assumptions (2020, background): “There are approaches for obtaining robust estimators for the fixed effects for cases of small sample size and for models with violated distributional assumptions (e.g. Kenward & Roger, 1997; Kenward & Roger, 2009; Royall, 1986; Royall & Tsou, 2003).”
On Using Bayesian Methods to Address Small Sample Problems (2016, methodology): “…methods like restricted maximum likelihood (REML) estimation (Harville, 1977), the Kenward–Roger standard error and degree of freedom correction (Kenward & Roger, 1997, 2009), or Skene–Kenward corrections (Skene & Kenward, 2010a, 2010b) that have been found to perform well in the case of small…”
Genetic Data Analysis for Plant and Animal Breeding (2017, methodology): “DDF i Requests computation of the approximate denominator degrees of freedom (df) according to (Kenward and Roger 1997, 2009) for the testing of fixed effects terms.”
The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration (2014, background): “Kenward and Roger (1997, 2009) showed by proof, simulation, and real-data example that their denominator degrees of freedom and covariance matrix adjustment can greatly reduce the underestimation of fixed-effect standard errors when the number of clusters is small in continuous outcome models with…”
Modeling Clustered Data with Very Few Clusters (2016, methodology): “Although Ferron et al. (2009) generally found that a Kenward-Roger correction was able to estimate standard errors appropriately even for extremely small numbers of clusters, the data-generation model in this study was much larger, and so the slight dip in performance was anticipated (e.g., McNeish ”
Rethinking the Analysis of Non-Normal Data in Plant and Soil Science (2015, methodology): “They, and the F and p values, reflect the procedure developed by Kenward and Roger (2009) to account for the effect of the covariance structure on degrees of freedom and standard errors. Although the Kenward–Roger adjustment was derived for the LMM with normally distributed data and is an ad hoc pro”
Multivariate meta-analysis for non-linear and other multi-parameter associations (2012, unknown): “Suitable adjustments for the underestimation of (co)variance error matrices due to the uncertainty in the estimation of ‰ are available [35, 36] although not yet been implemented in mvmeta.”