Inference in mixed models in R - beyond the usual asymptotic likelihood ratio test

Ulrich Halekoh, Søren Højsgaard

Research output: Contribution to conference without publisher/journalConference abstract for conferenceResearchpeer-review

Abstract

Mixed models in R (www.r-project.org) are currently usually handled with the lme4 package.
Until recently, inference (hypothesis test) in linear mixed models with lme4 was commonly based on the limiting χ2 distribution of the likelihood ratio statistic. The pbkrtest package provides two alternatives: 1) A Kenward-Roger approximation for calculating (or estimating) the numerator degrees of freedom for an ”F-like” test statistic. 2) p-values based on simulating the reference
distribution of the likelihood ratio statistic via parametric bootstrap. A recent addition to the package is a Satterthwaite approximation of the degrees of freedom. We will illustrate the package through various examples, and discuss some directions for further developments.

References
[1] Halekoh, U., Højsgaard, S. (2014). A Kenward-Roger Approximation and Parametric Bootstrap
Methods for Tests in Linear Mixed Models The R Package pbkrtest. Journal of Statistical
Software 59, 1–32.
36
Original languageEnglish
Publication date2018
Number of pages1
Publication statusPublished - 2018
Event27th Nordic Conference in Mathematical Statistics - Dorpat Convention Centre, Tartu, Estonia
Duration: 26 Jun 201829 Aug 2018
Conference number: 27
http://nordstat2018.ut.ee/

Conference

Conference27th Nordic Conference in Mathematical Statistics
Number27
LocationDorpat Convention Centre
CountryEstonia
CityTartu
Period26/06/201829/08/2018
Internet address

Keywords

  • Kenward-Roger approximation
  • parametric bootstrap
  • Satterthwaite approximation

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