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  • 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. (xsd:string)
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  • First published online: July 24, 2020, https://doi.org/10.1007/s11749-020-00727-x. (SILC) (xsd:string)
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  • 2021 (xsd:gyear)
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  • 2021 (xsd:gyear)
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  • 10.1007/s11749-020-00727-x ()
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  • 462–480 (xsd:string)
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  • 18638260 ()
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  • Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models (xsd:string)
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