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

Author

Listed:
  • Alessio Farcomeni

    (University of Rome “Tor Vergata”)

  • Monia Ranalli

    (Sapienza - University of Rome)

  • Sara Viviani

    (Viale delle Terme di Caracalla)

Abstract

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.

Suggested Citation

  • Alessio Farcomeni & Monia Ranalli & Sara Viviani, 2021. "Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 462-480, June.
    • Handle: RePEc:spr:testjl:v:30:y:2021:i:2:d:10.1007_s11749-020-00727-x
    DOI: 10.1007/s11749-020-00727-x
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