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Article
2024
Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient
Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient
Abstract (English)
The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.
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Theoretical and applied genetics, 137 (2024), 6, 134.
https://doi.org/10.1007/s00122-024-04639-4.
ISSN: 1432-2242
Berlin/Heidelberg : Springer Berlin Heidelberg
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Studnicki, M., & Piepho, H. P. (2024). Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient. Theoretical and applied genetics, 137(6). https://doi.org/10.1007/s00122-024-04639-4
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English
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Sustainable Development Goals
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@article{Studnicki2024,
doi = {10.1007/s00122-024-04639-4},
author = {Studnicki, Marcin and Piepho, Hans Peter},
title = {Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient},
journal = {Theoretical and Applied Genetics},
year = {2024},
volume = {137},
number = {6},
}