Nonparametric estimation of the mode of a distribution of random curves

• 成果类型: Article
• 署名作者: Gasser, T; Hall, P; Presnell, B
• 署名单位: Australian National University; University of Zurich
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/1467-9868.00148
• 发表日期: 1998
• 页码: 681-691
• 关键词: acceleration; bootstrap; GROWTH; tools

摘要

Motivated by the need to develop meaningful empirical approximations to a 'typical' data value, we introduce methods for density and mode estimation when data are in the form of random curves. Our approach is based on finite dimensional approximations via generalized Fourier expansions on an empirically chosen basis. The mode estimation problem is reduced to a problem of kernel-type multivariate estimation from vector data and is solved using a new recursive algorithm for finding the empirical mode. The algorithm may be used as an aid to the identification of clusters in a set of data curves. Bootstrap methods are employed to select the bandwidth.