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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Local influence diagnostics for hierarchical finite-mixture random-effects models
Trias Wahyuni Rakhmawati1, Geert Molenberghs1,2, Geert Verbeke1,2
1I-BioStat, Hasselt University, B-3500, Hasselt, Belgium.
Abstract:
The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. We also compare the two types of perturbation. Our results are illustrated using linear mixed models fitted to data from three studies. A simulation study is also conducted in order to strengthen the result from case studies.
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