Quantile hidden semi-Markov models for multivariate time series

Luca Merlo; Antonello Maruotti; Lea PetrellaAntonio Punzo

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Quantile hidden semi-Markov models for multivariate time series

机译：Quantile hidden semi-Markov models for multivariate time series

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Abstract This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate Asymmetric Laplace (MAL) distribution, which allows to model the quantiles of all univariate conditional distributions of a multivariate response simultaneously, incorporating the correlation structure among the outcomes. Unobserved serial heterogeneity across observations is modeled by introducing regime-dependent parameters that evolve according to a latent finite-state semi-Markov chain. Exploiting the hierarchical representation of the MAL, inference is carried out using an efficient Expectation-Maximization algorithm based on closed form updates for all model parameters, without parametric assumptions about the states’ sojourn distributions. The validity of the proposed methodology is analyzed both by a simulation study and through the empirical analysis of air pollutant concentrations in a small Italian city.

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来源
《Statistics and computing》 |2022年第4期|1-22|共22页
作者
Luca Merlo; Antonello Maruotti; Lea PetrellaAntonio Punzo;
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作者单位

Sapienza University of Rome;

University of Bergen||LUMSA University;

University of Catania;

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正文语种英语
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关键词
EM algorithm; Latent process; Maximum likelihood; Multivariate asymmetric Laplace distribution; Quantile regression; Sojourn distribution;

Quantile hidden semi-Markov models for multivariate time series

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