Weighted Estimation of AMMI and GGE Models
Weighted Estimation of AMMI and GGE Models
复制标题
AMMI 和 GGE 模型的加权估计
DOI：
10.1007/s13253-018-0323-z
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Piepho
中科院分区：
文献类型：
--
作者：
Hadasch;Forkman;Piepho
AMMI/GGE模型可用于描述基因型-环境平均值的双向表。当基因型-环境均值独立且同方差时，普通最小二乘（OLS）给出了模型的最优估计。在植物育种中，基因型-环境平均值的独立性和同方差性的假设经常被违反，然而，广义最小二乘（GLS）估计是更合适的。本文介绍了三种不同的GLS算法，使用一个加权矩阵，以考虑基因型与环境之间的相关性以及异方差。为了研究GLS估计的有效性，所提出的算法使用三种不同的加权矩阵来实现，包括（i）单位矩阵（OLS估计），（ii）基因型-环境平均值的完全逆协方差矩阵的近似，以及（iii）基因型-环境平均值的完全逆协方差矩阵。使用模拟数据建模的真实的实验，不同的加权方法进行了比较的基因型-环境的平均值，互作效应，奇异向量的均方误差。结果表明，加权估计一般优于未加权估计的均方误差。此外，加权估计的有效性增加时的异质性的基因型-环境的手段增加。
The AMMI/GGE model can be used to describe a two-way table of genotype–environment means. When the genotype–environment means are independent and homoscedastic, ordinary least squares (OLS) gives optimal estimates of the model. In plant breeding, the assumption of independence and homoscedasticity of the genotype–environment means is frequently violated, however, such that generalized least squares (GLS) estimation is more appropriate. This paper introduces three different GLS algorithms that use a weighting matrix to take the correlation between the genotype–environment means as well as heteroscedasticity into account. To investigate the effectiveness of the GLS estimation, the proposed algorithms were implemented using three different weighting matrices, including (i) an identity matrix (OLS estimation), (ii) an approximation of the complete inverse covariance matrix of the genotype–environment means, and (iii) the complete inverse covariance matrix of the genotype–environment means. Using simulated data modeled on real experiments, the different weighting methods were compared in terms of the mean-squared error of the genotype–environment means, interaction effects, and singular vectors. The results show that weighted estimation generally outperformed unweighted estimation in terms of the mean-squared error. Furthermore, the effectiveness of the weighted estimation increased when the heterogeneity of the variances of the genotype–environment means increased.
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影响因子：
3
作者：
GOLLOB, HF
通讯作者：
GOLLOB, HF
DOI：
10.1111/1467-9868.00201
发表时间：
1999-01-01
影响因子：
5.8
作者：
Besag, J;Higdon, D
通讯作者：
Higdon, D
DOI：
10.1007/s13253-011-0063-9
发表时间：
2012-04-01
影响因子：
1.4
作者：
Perez-Elizalde, Sergio;Jarquin, Diego;Crossa, Jose
通讯作者：
Crossa, Jose
影响因子：
2.3
作者：
Hadasch, Steffen;Forkman, Johannes;Piepho, Hans-Peter
通讯作者：
Piepho, Hans-Peter
影响因子：
5.4
作者：
GAUCH, HG;ZOBEL, RW
通讯作者：
ZOBEL, RW