We propose a hidden Markov model for longitudinal multivariate continuous responses, accounting for missing data under the missing at random assumption. Maximum likelihood estimation of this model is carried out through the Expectation-Maximization algorithm. To address the problem of dimensionality reduction, we develop a greedy search algorithm based on the Bayesian Information Criterion. We illustrate the proposal through a dataset collected by the World Bank and UNESCO Institute for Statistics on the basis of which we dynamically cluster countries according to the selected variables observed during the period 2000-2017.
Pennoni, F., Bartolucci, F., Pandolfi, S. (2021). A Hidden Markov Model for Variable Selection with Missing Values.. In Book of short papers of the 50th Scientific Meeting of the Italian Statistical Society (pp. 145-150). Pearson.
A Hidden Markov Model for Variable Selection with Missing Values.
Pennoni, F;
2021
Abstract
We propose a hidden Markov model for longitudinal multivariate continuous responses, accounting for missing data under the missing at random assumption. Maximum likelihood estimation of this model is carried out through the Expectation-Maximization algorithm. To address the problem of dimensionality reduction, we develop a greedy search algorithm based on the Bayesian Information Criterion. We illustrate the proposal through a dataset collected by the World Bank and UNESCO Institute for Statistics on the basis of which we dynamically cluster countries according to the selected variables observed during the period 2000-2017.
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