TRANSFORMATIONS IN DENSITY-ESTIMATION

• 成果类型: Article
• 署名作者: WAND, MP; MARRON, JS; RUPPERT, D
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; Cornell University; Cornell University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290569
• 发表日期: 1991
• 页码: 343-353
• 关键词: kurtosis

摘要

For the density estimation problem the global window width kernel density estimator does not perform well when the underlying density has features that require different amounts of smoothing at different locations. In this article we propose to transform the data with the intention that a global window width is more appropriate for the density of the transformed data. The density estimate of the original data is the ''back-transform'' by change of variables of the global window width estimate of the transformed data's density. We explore choosing the transformation from suitable parametric families. Data-based selection rules for the choice of transformations and the window width are discussed. Application to real and simulated data demonstrates the usefulness of our proposals.