To refer to this page use:
|Abstract:||Many statistical methods gain robustness and flexibility by sacrificing convenient computational structures. In this article, we illustrate this fundamental tradeoff by studying a semiparametric graph estimation problem in high dimensions. We explain how novel computational techniques help to solve this type of problem. In particular, we propose a nonparanormal neighborhood pursuit algorithm to estimate high-dimensional semiparametric graphical models with theoretical guarantees. Moreover, we provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Though this article focuses on the problem of graph estimation, the proposed methodology is widely applicable to other problems with similar structures. We also report thorough experimental results on text, stock, and genomic datasets.|
|Electronic Publication Date:||20-Oct-2014|
|Citation:||Zhao, Tuo, Kathryn Roeder, and Han Liu. "Positive semidefinite rank-based correlation matrix estimation with application to semiparametric graph estimation." Journal of Computational and Graphical Statistics 23, no. 4 (2014): 895-922.|
|Pages:||895 - 922|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Journal of Computational and Graphical Statistics|
Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.