Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models

• Alessio Farcomeni ^[1] ; Monia Ranalli ^[2] ; Sara Viviani ^[3]
  1. [1] University of Rome “Tor Vergata”, Rome, Italy
  2. [2] Sapienza - University of Rome, Rome, Italy
  3. [3] Food and Agriculture Organization of the United Nations, Statistics Division (ESS), Viale delle Terme di Caracalla, 4 - 00153, Rome, Italy

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 30, Nº. 2, 2021, págs. 462-480
• Idioma: inglés
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• Resumen

  • 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.