具有潜在聚类的线性混合效应模型的模型适应度和预测精度

Y. Bello, W. B. Yahya, A. Abdulraheem
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引用次数: 1

摘要

在聚类数据中,一个聚类内的观测值之间表现出相似性,因为它们具有与其他聚类中观测值不同的共同特征。在一个给定的群体中,可能会出现不同的聚类,因为相关性可能出现在多个维度上。现有的原始线性混合效应模型的多级分析技术仅限于自然聚类,这些聚类在现实生活中往往不现实。因此,本文提出了双线性混合模型(dlmm),用于对数据集中存在的未观察到的潜在聚类进行建模,以在模型适应度和预测精度方面获得可观的收益。该方法利用多变量聚类分析,探索了基于自然聚类衍生的潜在聚类的双线性混合模型(dlmm)的开发和分析。一组已发表的政治分析数据被用来证明所提出模型的有效性。所提出的dlmm模型获得了模型评估准则(赤池信息准则、贝叶斯信息准则和均方根误差)的最小值,因此在模型适应度和预测精度方面优于经典plmm模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model Fitness and Predictive Accuracy in Linear Mixed-Effects Models with Latent Clusters
In clustered data, observations within a cluster show similarity between themselves because they share common features different from observations in the other clusters. In a given population, different clustering may surface because correlation may occur across more than one dimension. The existing multilevel analysis techniques of the primal linear mixed-effect models are limited to natural clusters which are often not realistic to capture in real-life situations. Therefore, this paper proposes dual linear mixed models (DLMMs) for modeling unobserved latent clusters when such are present in data sets to yield appreciable gains in model fitness and predictive accuracy. The methodology explored the development and analysis of the dual linear mixed models (DLMMs) based on the derived latent clusters from the natural clusters using multivariate cluster analysis. A published data set on political analysis was used to demonstrate the efficiency of the proposed models. The proposed DLMMs have yielded minimum values of the models' assessment criteria (Akaike information criterion, Bayesian information criterion, and root mean squared error), and hence, outperformed the classical PLMMs in terms of model fitness and predictive accuracy.
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