Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)
Han Liu, Fang Han, Cun-hui Zhang
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 estima- tion. 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).