An improved estimator of the density function at the boundary

• 成果类型: Article
• 署名作者: Zhang, S; Karunamuni, RJ; Jones, MC
• 署名单位: University of Alaska System; University of Alaska Fairbanks; University of Alberta; Open University - UK
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2669937
• 发表日期: 1999
• 页码: 1231-1241
• 关键词: end-points; transformations

摘要

We propose a new method of boundary correction for kernel density estimation. The technique is a kind of generalized reflection method involving reflecting a transformation of the data. The transformation depends on a pilot estimate of the logarithmic derivative of the density at the boundary. In simulations, the new method is seen to clearly outperform an earlier generalized reflection idea. It also has overall advantages over boundary kernel methods and a nonnegative adaptation thereof, although the latter are competitive in some situations. We also present the theory underlying the new methodology.