期刊
ELECTRONIC JOURNAL OF STATISTICS
卷 5, 期 -, 页码 981-1014出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/11-EJS634
关键词
Covariance selection; Gaussian graphical model; mixture model; Dirichlet process; hidden Markov model; nonparametric Bayes inference
资金
- National Science Foundation [DMS 0915272, DMS 1120255]
- German Research Foundation (DFG) [GRK 1653]
- Direct For Mathematical & Physical Scien [0915272] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [0915272] Funding Source: National Science Foundation
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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