Estimating Multivariate Latent-Structure Models

Stéphane Bonhomme, Koen Jochmans and Jean-Marc Robin
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Stéphane Bonhomme: University of Chicago

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Abstract: A constructive proof of identification of multilinear decompositions of multiway arrays is presented. It can be applied to show identification in a variety of multivariate latent structures. Examples are finite-mixture models and hidden Markov models. The key step to show identification is the joint diagonalization of a set of matrices in the same non-orthogonal basis. An estimator of the latent-structure model may then be based on a sample version of this joint-diagonalization problem. Algorithms are available for computation and we derive distribution theory. We further develop asymptotic theory for orthogonal-series estimators of component densities in mixture models and emission densities in hidden Markov models.

Keywords: Estimators; Markov models; Diagonal arguments (search for similar items in EconPapers)
Date: 2016
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-03392022
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Citations: View citations in EconPapers (7)

Published in Annals of Statistics, 2016, 44 (2), pp.540 - 563. ⟨10.1214/15-AOS1376⟩

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Related works:
Working Paper: Estimating Multivariate Latent-Structure Models (2016)
Working Paper: Estimating Multivariate Latent-Structure Models (2014)
Working Paper: Estimating Multivariate Latent-Structure Models (2014)
Working Paper: Estimating Multivariate Latent-Structure Models (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03392022

DOI: 10.1214/15-AOS1376

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