Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 5 (2011), 981-1014.
Sparse covariance estimation in heterogeneous samples
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.
Electron. J. Statist., Volume 5 (2011), 981-1014.
First available in Project Euclid: 15 September 2011
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62F15: Bayesian inference 62H25: Factor analysis and principal components; correspondence analysis
Secondary: 62H30: Classification and discrimination; cluster analysis [See also 68T10, 91C20] 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Rodríguez, Abel; Lenkoski, Alex; Dobra, Adrian. Sparse covariance estimation in heterogeneous samples. Electron. J. Statist. 5 (2011), 981--1014. doi:10.1214/11-EJS634. https://projecteuclid.org/euclid.ejs/1316092866