ATS METHODS - NONPARAMETRIC REGRESSION FOR NON-GAUSSIAN DATA

• 成果类型: Article
• 署名作者: CLEVELAND, WS; MALLOWS, CL; MCRAE, JE
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290771
• 发表日期: 1993
• 页码: 821-835
• 关键词: LOCALLY WEIGHTED REGRESSION; generalized linear-models; densities

摘要

ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the ''A'' in ATS) is carried out, then a variance-stabilizing transformation is applied (''T''), and finally the result is smoothed (''S'') using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for fitting a surface when the response is binary, for estimating density, and for estimating the spectrum of a time series. We also present some theoretical investigations that give guidance on how to choose the amount of averaging and how efficient the methods are.