Classified Mixed Model Prediction
Classified Mixed Model Prediction
复制标题
分类混合模型预测
DOI：
10.1080/01621459.2016.1246367
复制
发表时间：
2018
影响因子：
3.7
通讯作者：
Thuan Nguyen
中科院分区：
文献类型：
--
作者：
Jiming Jiang;J. Sunil Rao;J. Fan;Thuan Nguyen
摘要 许多实际问题都与预测有关，其中主要兴趣在于受试者（例如个性化医疗）或（小）子群体（例如小社区）层面。在这种情况下，通过识别新主题所属的类别可以显着提高预测准确性。这样，新的受试者就可能与训练数据中同一类别对应的随机效应相关联，从而可以使用混合模型预测的方法做出最佳预测。我们提出了一种称为分类混合模型预测（CMMP）的新方法来实现这一目标。我们开发 CMMP 用于预测混合效应和预测未来观察，并考虑训练数据主题中新主题可能存在或可能不“匹配”的不同场景。通过理论和实证研究来研究 CMMP 的特性，包括基于 CMMP 的预测区间及其与现有方法的比较。特别是，我们表明，即使新观测值的类别与训练数据的类别之间不存在实际匹配，CMMP 仍然有助于提高预测精度。考虑两个实际数据示例。本文的补充材料可在线获取。
ABSTRACT Many practical problems are related to prediction, where the main interest is at subject (e.g., personalized medicine) or (small) sub-population (e.g., small community) level. In such cases, it is possible to make substantial gains in prediction accuracy by identifying a class that a new subject belongs to. This way, the new subject is potentially associated with a random effect corresponding to the same class in the training data, so that method of mixed model prediction can be used to make the best prediction. We propose a new method, called classified mixed model prediction (CMMP), to achieve this goal. We develop CMMP for both prediction of mixed effects and prediction of future observations, and consider different scenarios where there may or may not be a “match” of the new subject among the training-data subjects. Theoretical and empirical studies are carried out to study the properties of CMMP, including prediction intervals based on CMMP, and its comparison with existing methods. In particular, we show that, even if the actual match does not exist between the class of the new observations and those of the training data, CMMP still helps in improving prediction accuracy. Two real-data examples are considered. Supplementary materials for this article are available online.
影响因子：
5.9
作者：
Donald Hedeker;Robert D. Gibbons;Brian R. Flay
通讯作者：
Donald Hedeker;Robert D. Gibbons;Brian R. Flay
影响因子：
2.1
作者：
Elliott, MR;Gallo, JJ;Katz, IR
通讯作者：
Katz, IR