Electronic Journal of Statistics

On strong identifiability and convergence rates of parameter estimation in finite mixtures

Nhat Ho and XuanLong Nguyen

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This paper studies identifiability and convergence behaviors for parameters of multiple types, including matrix-variate ones, that arise in finite mixtures, and the effects of model fitting with extra mixing components. We consider several notions of strong identifiability in a matrix-variate setting, and use them to establish sharp inequalities relating the distance of mixture densities to the Wasserstein distances of the corresponding mixing measures. Characterization of identifiability is given for a broad range of mixture models commonly employed in practice, including location-covariance mixtures and location-covariance-shape mixtures, for mixtures of symmetric densities, as well as some asymmetric ones. Minimax lower bounds and rates of convergence for the maximum likelihood estimates are established for such classes, which are also confirmed by simulation studies.

Article information

Electron. J. Statist., Volume 10, Number 1 (2016), 271-307.

Received: February 2015
First available in Project Euclid: 17 February 2016

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62F15: Bayesian inference 62G05: Estimation
Secondary: 62G20: Asymptotic properties

Mixture models strong identifiability Wasserstein distances minimax bounds maximum likelihood estimation


Ho, Nhat; Nguyen, XuanLong. On strong identifiability and convergence rates of parameter estimation in finite mixtures. Electron. J. Statist. 10 (2016), no. 1, 271--307. doi:10.1214/16-EJS1105. https://projecteuclid.org/euclid.ejs/1455715963

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