Abstract
AbstractWe 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 implementation of our approach can be downloaded from .
Funder
Università degli Studi di Roma La Sapienza
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference49 articles.
1. Aitchison J (2011) The statistical analysis of compositional data. Monographs on statistics and applied probability. Springer, New York
2. Anderson G, Farcomeni A, Pittau MG, Zelli R (2019a) Multidimensional nation wellbeing, more equal yet more polarized: an analysis of the progress of human development since 1990. J Econ Dev 44:00–11
3. Anderson G, Farcomeni A, Pittau MG, Zelli R (2019b) Rectangular latent Markov models for time-specific clustering, with an analysis of the well being of nations. J R Stat Soc (Ser C) 68:603–621
4. Ando T, Bai J (2017) Clustering huge number of financial time series: a panel data approach with high-dimensional predictors and factor structures. J Am Stat Assoc 112:1182–1198
5. Atkinson AB (2003) Multidimensional deprivation: contrasting social welfare and counting approaches. J Econ Inequal 1:51–65
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