Robust estimators for nondecomposable elliptical graphical models

• 成果类型: Article
• 署名作者: Vogel, D.; Tyler, D. E.
• 署名单位: Ruhr University Bochum; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu041
• 发表日期: 2014
• 页码: 865882
• 关键词: maximum-likelihood-estimation; multivariate time-series; COVARIANCE-MATRIX; asymptotic-behavior; gaussian models; scatter; selection; location; distributions; EFFICIENCY

摘要

Robust estimators of the restricted covariance matrices associated with elliptical graphical models are studied. General asymptotic results, which apply to both decomposable and nondecomposable graphical models, are presented for robust plug-in type estimators. These extend results previously established only for the decomposable case. Furthermore, a class of graphical M-estimators for the restricted covariance matrices is introduced and compared with the corresponding plug-in M-estimators. The two approaches are shown to be asymptotically equivalent under random sampling from an elliptical distribution. A simulation study demonstrates the superiority of the graphical M-estimators for small samples.