From Depth to Local Depth: A Focus on Centrality

• 成果类型: Article
• 署名作者: Paindaveine, Davy; Van Bever, Germain
• 署名单位: Universite Libre de Bruxelles; Universite Libre de Bruxelles
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.813390
• 发表日期: 2013
• 页码: 1105-1119
• 关键词: maximum depth; MULTIVARIATE; contours; notion; CONVERGENCE; quantiles

摘要

Aiming at analyzing multimodal or nonconvexly supported distributions through data depth, we introduce a local extension of depth. Our construction is obtained by conditioning the distribution to appropriate depth-based neighborhoods and has the advantages, among others, of maintaining affine-invariance and applying to all depths in a generic way. Most importantly, unlike their competitors, which (for extreme localization) rather measure probability mass, the resulting local depths focus on centrality and remain of a genuine depth nature at any locality level. We derive their main properties, establish consistency of their sample versions, and study their behavior under extreme localization. We present two applications of the proposed local depth (for classification and for symmetry testing), and we extend our construction to the regression depth context. Throughout, we illustrate the results on several datasets, both artificial and real, univariate and multivariate. Supplementary materials for this article are available online.