TEST ( IF 1.3 ) Pub Date : 2020-07-24 , DOI: 10.1007/s11749-020-00727-x Alessio Farcomeni , Monia Ranalli , Sara Viviani
We present a method for dimension reduction of multivariate longitudinal data, where new variables are assumed to follow a latent Markov model. New variables are obtained as linear combinations of the multivariate outcome as usual. Weights of each linear combination maximize a measure of separation of the latent intercepts, subject to orthogonality constraints. We evaluate our proposal in a simulation study and illustrate it using an EU-level data set on income and living conditions, where dimension reduction leads to an optimal scoring system for material deprivation. An R implementation of our approach can be downloaded from https://github.com/afarcome/LMdim.
中文翻译:
通过优化投影隐马尔可夫模型的类分离来减少纵向多元数据的维数
我们提出了一种用于多维纵向数据降维的方法,其中假定新变量遵循潜在的马尔可夫模型。照常将新变量作为多元结果的线性组合获得。每个线性组合的权重在正交性约束下最大化了潜在截距的分离程度。我们在模拟研究中评估了我们的建议,并使用了欧盟级别的有关收入和生活条件的数据集进行了说明,其中减少维度可导致针对物质匮乏的最佳评分系统。可以从https://github.com/afarcome/LMdim下载我们方法的R实现。