A comparison of higher-order bias kernel density estimators

• 成果类型: Article
• 署名作者: Jones, MC; Signorini, DF
• 署名单位: University of Edinburgh
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2965571
• 发表日期: 1997
• 页码: 1063-1073
• 关键词: reduction method; transformations; rules

摘要

We consider many kernel-based density estimators, all theoretically improving bias from O(h(2)), as the smoothing parameter h --> 0, to O(h(4)). Examples include higher-order kernels, variable kernel methods, and transformation and multiplicative bias-correction approaches. We stress the similarities between what appear to be disparate approaches. In particular, we show how the mean squared errors of all methods have the same form. Our main practical contribution is a comparative simulation study that isolates the most promising approaches. It remains debatable, however, as to whether even the best methods give worthwhile improvements, at least for small-to-moderate sample exploratory purposes.