Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models
Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models
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DOI：
10.1007/s11749-020-00727-x
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发表时间：
2020-07-24
期刊：
影响因子：
1.3
通讯作者：
Viviani, Sara
中科院分区：
文献类型：
--
作者：
Farcomeni, Alessio;Ranalli, Monia;Viviani, Sara
本文提出了一种多变量纵向数据降维的方法，其中新的变量被假定为遵循隐马尔可夫模型。新的变量像往常一样作为多变量结果的线性组合获得。每个线性组合的权重最大化受正交约束的潜在截距的分离的度量。我们在模拟研究中评估我们的建议，并使用欧盟层面的收入和生活条件数据集进行说明，其中降维导致物质匮乏的最佳评分系统。我们的方法的R实现可以从https://github.com/afarcome/LMdim下载。
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.