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

Alessio Farcomeni, Monia Ranalli () and Sara Viviani
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Monia Ranalli: Sapienza - University of Rome
Sara Viviani: Viale delle Terme di Caracalla

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 30, issue 2, No 10, 462-480

Abstract: 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.

Keywords: Dimension reduction; EU-SILC; Material deprivation; Multivariate longitudinal data; Orthogonality; 62H25; 62H30; 62J05 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11749-020-00727-x

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