Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models

By Fabian Scheipl, Sonja Greven, Helmut Küchenhoff. Computational Statistics & Data Analysis, 2008

314 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c4828c94fe3db36f558b5b522415d34ed9ea11cb

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In Artificial Intelligence

In Management Science and Operations Research

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Citations in the corpus

Restricted Likelihood Ratio Testing for Zero Variance Components in Linear Mixed Models (2008, methodology): “In some cases, one might still want to use a simple parametric bootstrap to determine the distribution of the RLRT.”
Best-Practice Recommendations for Estimating Cross-Level Interaction Effects Using Multilevel Modeling (2013, methodology): “Appendix A includes an R function in the RLRsim package for testing the statistical significance of variance components (Scheipl et al., 2008).”
On p-values for smooth components of an extended generalized additive model (2013, background): “…choice for r. Existing work on the testing problem includes Cox et al. (1988), Liu & Wang (2004), Zhang & Lin (2003), Crainiceanu et al. (2005), Scheipl et al. (2008) and Nummi et al. (2011), but has usually focused on the hypothesis that Fj is a simple polynomial in the null space of the…”
A general framework for functional regression modelling (2017, background): “This also holds—at least to some extent—for inference in such models (e.g., Greven et al., 2008; Scheipl et al., 2008; Wood, 2013) and its transfer to functional regression (e.g., Staicu et al., 2014; Swihart et al., 2014; McLean et al., 2015).”
Odonata as candidate macroecological barometers for global climate change (2015, methodology): “…significance of the fixed effect was evaluated using the pamer. fnc function in the LMERConvenienceFunctions package (Tremblay and Ransijn 2013) in R, and the statistical significance of the random effect was evaluated using the exactRLRT function in the RLRsim package (Scheipl et al. 2008) in R.”
Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis (2014, methodology): “The R package RLRsim (Scheipl et al., 2008) or a MATLAB function http://www.biostat. jhsph.edu/~ccrainic/software.html can be used for implementation of Algorithm 1.”
An Introduction to Mixed Models for Experimental Psychology (2019, unknown): “However, it should be able to control for Type I error better than the LRT. Note that parametric bootstrap and the LRT are procedures that can also be used to test the parameters associated to the random e ﬀ ects (Matuschek et al., 2017; see also Scheipl et al., 2008).”
A simple test for random effects in regression models (2013, methodology): “The simulation study of Scheipl et al. (2008) was repeated, to compare the test proposed here with a test based on simulation of the correct distribution of the likelihood ratio statistic, and with the best test found in Scheipl.”