Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures
Empirical best linear unbiased prediction in cultivar trials using factor-analytic variance-covariance structures
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DOI：
10.1007/s001220050885
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发表时间：
1998-07-01
影响因子：
5.4
通讯作者：
Piepho, HP
中科院分区：
文献类型：
--
作者：
Piepho, HP
评估新植物品种的多环境试验的结果可以按环境显示在基因型双向表中。可以使用不同的估计器来填充此类表格的单元格。先前已经表明，通过环境平均值来预测简单基因型的准确性通常低于其他估计量，例如基于乘法模型的最小二乘估计量，例如加性主效应乘性交互作用 (AMMI) 模型，或基于双向方差分析 (ANOVA) 模型的经验最佳线性无偏预测器 (BLUP)。本文提出了一种基于乘法项模型获得 BLUP 的方法。通过使用五个真实数据集（油菜、甘蓝型油菜）的交叉验证表明，基于乘法项模型的 BLUP 的预测精度可能优于基于相同模型的最小二乘估计器，也优于基于 ANOVA 模型的 BLUP。
Results of multi-environment trials to evaluate new plant cultivars may be displayed in a two-way table of genotypes by environments. Different estimators are available to fill the cells of such tables. It has been shown previously that the predictive accuracy of the simple genotype by environment mean is often lower than that of other estimators, e.g. least-squares estimators based on multiplicative models, such as the additive main effects multiplicative interaction (AMMI) model, or empirical best-linear unbiased predictors (BLUPs) based on a two-way analysis-of-variance (ANOVA) model. This paper proposes a method to obtain BLUPs based on models with multiplicative terms. It is shown by cross-validation using five real data sets (oilseed rape, Brassica napus L.) that the predictive accuracy of BLUPs based on models with multiplicative terms may be better than that of least-squares estimators based on the same models and also better than BLUPs based on ANOVA models.