Quantile hidden semi-Markov models for multivariate time series.

Luca Merlo, Antonello Maruotti, Lea Petrella, Antonio Punzo

Abstract read

In one paragraph

Article in Statistics and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Luca MerloDepartment of Statistical Sciences, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy.ORCID 0000-0001-5267-5390

Antonello MaruottiDepartment of Mathematics, University of Bergen, Bergen, Norway.

Lea PetrellaMEMOTEF Department, Sapienza University of Rome, Rome, Italy.

Antonio PunzoDepartment of Economics and Business, University of Catania, Catania, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-022-10130-1.

Indexed as

EM algorithmLatent processMaximum likelihoodMultivariate asymmetric Laplace distributionQuantile regressionSojourn distribution

Identifiers

PMID35968041

DOI10.1007/s11222-022-10130-1

PMCPMC9360757

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.