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Erratum to: Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data

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  • Fulvia Pennoni

    (University of Milano-Bicocca)

  • Francesco Bartolucci

    (University of Perugia)

  • Silvia Pandolfi

    (University of Perugia)

Abstract

We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a given time occasion. Through this procedure, we achieve a dimensionality reduction by selecting the subset of the most informative responses for clustering individuals and simultaneously choosing the optimal number of these clusters corresponding to latent states. The approach is based on comparing different model specifications in terms of the subset of responses assumed to be dependent on the latent states, and it relies on a greedy search algorithm based on the Bayesian information criterion seen as an approximation of the Bayes factor. A suitable expectation-maximization algorithm is employed to obtain maximum likelihood estimates of the model parameters under the missing-at-random assumption. The proposal is illustrated via Monte Carlo simulation and an application where development indicators collected over eighteen years are selected, and countries are clustered into groups to evaluate their growth over time.
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Suggested Citation

  • Fulvia Pennoni & Francesco Bartolucci & Silvia Pandolfi, 2024. "Erratum to: Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 590-590, November.
    • Handle: RePEc:spr:jclass:v:41:y:2024:i:3:d:10.1007_s00357-024-09464-4
    DOI: 10.1007/s00357-024-09464-4
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    2. Lynge Nielsen, 2013. "How to Classify Countries Based on Their Level of Development," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(3), pages 1087-1107, December.
    3. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.
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    7. Abby Flynt & Nema Dean, 2019. "Growth Mixture Modeling with Measurement Selection," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 3-25, April.
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