COMPARING NONPARAMETRIC VERSUS PARAMETRIC REGRESSION FITS

• 成果类型: Article
• 署名作者: HARDLE, W; MAMMEN, E
• 署名单位: Humboldt University of Berlin
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176349403
• 发表日期: 1993
• 页码: 1926-1947
• 关键词: resampling methods; models; GOODNESS; bootstrap; jackknife; GROWTH

摘要

In general, there will be visible differences between a parametric and a nonparametric curve estimate. It is therefore quite natural to compare these in order to decide whether the parametric model could be justified. An asymptotic quantification is the distribution of the integrated squared difference between these curves. We show that the standard way of boot-strapping this statistic fails. We use and analyse a different form of bootstrapping for this task. We call this method the wild bootstrap and apply it to fitting Engel curves in expenditure data analysis.