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Testing random effects in linear mixed models: another look at the F-test (with discussion)

Date

2019

Authors

Hui, Francis
Muller, Samuel
Welsh, Alan

Journal Title

Journal ISSN

Volume Title

Publisher

Blackwell Publishing Ltd

Abstract

This article re‐examines the F‐test based on linear combinations of the responses, or FLC test, for testing random effects in linear mixed models. In current statistical practice, the FLC test is underused and we argue that it should be reconsidered as a valuable method for use with linear mixed models. We present a new, more general derivation of the FLC test which applies to a broad class of linear mixed models where the random effects can be correlated. We highlight three advantages of the FLC test that are often overlooked in modern applications of linear mixed models, namely its computation speed, its generality, and its exactness as a test. Empirical studies provide new insight into the finite sample performance of the FLC test, identifying cases where it is competitive or even outperforms modern methods in terms of power, as well as settings in which it performs worse than simulation‐based methods for testing random effects. In all circumstances, the FLC test is faster to compute.

Description

Keywords

bootstrap, error contrasts, hypothesis testing, restricted maximum likelihood, variance components

Citation

Source

Australian and New Zealand Journal of Statistics

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1111/anzs.12256

Restricted until

2099-12-31