Open Access
2011 Sparse covariance estimation in heterogeneous samples
Abel Rodríguez, Alex Lenkoski, Adrian Dobra
Electron. J. Statist. 5: 981-1014 (2011). DOI: 10.1214/11-EJS634


Standard Gaussian graphical models implicitly assume that the conditional independence among variables is common to all observations in the sample. However, in practice, observations are usually collected from heterogeneous populations where such an assumption is not satisfied, leading in turn to nonlinear relationships among variables. To address such situations we explore mixtures of Gaussian graphical models; in particular, we consider both infinite mixtures and infinite hidden Markov models where the emission distributions correspond to Gaussian graphical models. Such models allow us to divide a heterogeneous population into homogenous groups, with each cluster having its own conditional independence structure. As an illustration, we study the trends in foreign exchange rate fluctuations in the pre-Euro era.


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Abel Rodríguez. Alex Lenkoski. Adrian Dobra. "Sparse covariance estimation in heterogeneous samples." Electron. J. Statist. 5 981 - 1014, 2011.


Published: 2011
First available in Project Euclid: 15 September 2011

zbMATH: 1274.62207
MathSciNet: MR2836767
Digital Object Identifier: 10.1214/11-EJS634

Primary: 62F15 , 62H25
Secondary: 62H30 , 62M10

Keywords: covariance selection , Dirichlet process , Gaussian graphical model , Hidden Markov model , mixture model , nonparametric Bayes inference

Rights: Copyright © 2011 The Institute of Mathematical Statistics and the Bernoulli Society

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