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 language | English |
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Pages (from-to) | 521-539 |
Number of pages | 19 |
Journal | Canadian Journal of Statistics |
Volume | 36 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Jan 2008 |
Externally published | Yes |
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