Bootstrapping data with multiple levels of variation

Christopher A. Field, Zhen Pang, Alan H. Welsh

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

The authors consider general estimators for the mean and variance parameters in the random effect model and in the transformation model for data with multiple levels of variation. They show that these estimators have different distributions under the two models unless all the variables have Gaussian distributions. They investigate the asymptotic properties of bootstrap procedures designed for the two models. They also report simulation results and illustrate the bootstraps using data on red spruce trees.
Original languageEnglish
Pages (from-to)521-539
Number of pages19
JournalCanadian Journal of Statistics
Volume36
Issue number4
DOIs
Publication statusPublished - 1 Jan 2008
Externally publishedYes

Keywords

  • Best linear unbiased prediction hierarchical
  • Data
  • Mixed model
  • Quasi-likelihood estimation
  • Random effect
  • Unbalanced data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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