@inproceedings{008240db91404414a3dd0589faf0f5ba,
title = "Transelliptical graphical models",
abstract = "We advocate the use of a new distribution family-the transelliptical-for robust inference of high dimensional graphical models. The transelliptical family is an extension of the nonparanormal family proposed by Liu et al. (2009). Just as the nonparanormal extends the normal by transforming the variables using univariate functions, the transelliptical extends the elliptical family in the same way. We propose a nonparametric rank-based regularization estimator which achieves the parametric rates of convergence for both graph recovery and parameter estimation. Such a result suggests that the extra robustness and flexibility obtained by the semiparametric transelliptical modeling incurs almost no efficiency loss. We also discuss the relationship between this work with the transelliptical component analysis proposed by Han and Liu (2012).",
author = "Han Liu and Fang Han and Zhang, {Cun Hui}",
year = "2012",
language = "English (US)",
isbn = "9781627480031",
series = "Advances in Neural Information Processing Systems",
pages = "800--808",
booktitle = "Advances in Neural Information Processing Systems 25",
note = "26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 ; Conference date: 03-12-2012 Through 06-12-2012",
}