Fiducial generalized p-values for testing zero-variance components in linear mixed-effects models

Xinmin Li, Haiyan Su, Hua Liang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Linear mixed-effects models are widely used in analysis of longitudinal data. However, testing for zero-variance components of random effects has not been well resolved in statistical literature, although some likelihood-based procedures have been proposed and studied. In this article, we propose a generalized p-value based method in coupling with fiducial inference to tackle this problem. The proposed method is also applied to test linearity of the nonparametric functions in additive models. We provide theoretical justifications and develop an implementation algorithm for the proposed method. We evaluate its finite-sample performance and compare it with that of the restricted likelihood ratio test via simulation experiments. We illustrate the proposed approach using an application from a nutritional study.

Original languageEnglish
Pages (from-to)1303-1318
Number of pages16
JournalScience China Mathematics
Volume61
Issue number7
DOIs
StatePublished - 1 Jul 2018

Keywords

  • 62G10
  • 62J10
  • fiducial distribution
  • generalized pivotal quantity
  • generalized test variable
  • penalized spline additive models
  • restricted likelihood ratio test
  • structural equation
  • zero-variance components

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