Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood

David M. Zucker*, Offer Lieberman, Orly Manor

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

The mixed linear model is a popular method for analysing unbalanced repeated measurement data. The classical statistical tests for parameters in this model are based on asymptotic theory that is unreliable in the small samples that are often encountered in practice. For testing a given fixed effect parameter with a small sample, we develop and investigate refined likelihood ratio (LR) tests. The refinements considered are the Bartlett correction and use of the Cox-Reid adjusted likelihood; these are examined separately and in combination. We illustrate the various LR tests on an actual data set and compare them in two simulation studies. The conventional LR test yields type 1 error rates that are higher than nominal. The adjusted LR test yields rates that are lower than nominal, with absolute accuracy similar to that of the conventional LR test in the first simulation study and better in the second. The Bartlett correction substantially improves the accuracy of the type 1 error rates with either the conventional or the adjusted LR test. In many cases, error rates that are very close to nominal are achieved with the refined methods.

Original languageAmerican English
Pages (from-to)827-838
Number of pages12
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume62
Issue number4
DOIs
StatePublished - 2000

Keywords

  • Higher order asymptotics
  • Repeated measurements

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