IMPUTING MISSING YIELD TRIAL DATA
IMPUTING MISSING YIELD TRIAL DATA
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DOI：
10.1007/bf00224240
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发表时间：
1990-01-01
影响因子：
5.4
通讯作者：
ZOBEL, RW
中科院分区：
文献类型：
--
作者：
GAUCH, HG;ZOBEL, RW
加性主效应与乘性互作（AMMI）统计模型已被证明是理解产量的基因型-环境互作、更准确地估计产量、更可靠地选择上级基因型以及允许更灵活和高效的试验设计的有效方法。然而，AMMI要求每个基因型和环境组合或处理的数据，即，缺失的数据是不可接受的。本文件讨论这个问题。尽管存在缺失数据，但仍实施期望最大化（EM）算法来拟合AMMI。这种缺失数据的AMMI版本在这里被称为“EM-AMMI”。EM-AMMI用于量化产量试验中的直接和间接信息，为观察到的准确性增益和填补缺失数据的过程提供理论见解。对于给定的处理，直接产量数据是该处理的重复，间接数据是试验中的所有其他产量数据。EM-AMMI用于输入纽约大豆产量试验的缺失数据。重要的应用来自无意和有意的数据缺失。经验测量证明了良好的预测成功，统计理论将这种成功归因于斯坦效应。
The Additive Main effects and Multiplicative Interaction (AMMI) statistical model has been demonstrated effective for understanding genotype-environment interactions in yields, estimating yields more accurately, selecting superior genotypes more reliably, and allowing more flexible and efficient experimental designs. However, AMMI had required data for every genotype and environment combination or treatment, i.e., missing data were inadmissible. The present paper addresses the problem. The Expectation-Maximization (EM) algorithm is implemented for fitting AMMI despite missing data. This missing-data version of AMMI is here termed "EM-AMMI. EM-AMMI is used to quantify the direct and indirect information in a yield trial, providing theoretical insight into the gain in accuracy observed and into the process of imputing missing data. For a given treatment, the direct yield data are the replicates of that treatment, and the indirect data are all the other yield data in the trial. EM-AMMI is used to input missing data for a New York soybean yield trial. Important applications arise from both unintentional and intentional missing data. Empirical measurements demonstrate good predictive success, and statistical theory attributes this success to the Stein effect.