ATS Methods: Nonparametric Regression for Non-Gaussian Data
作者:
WilliamS. Cleveland,
ColinL. Mallows,
JeanE. McRae,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1993)
卷期:
Volume 88,
issue 423
页码: 821-835
ISSN:0162-1459
年代: 1993
DOI:10.1080/01621459.1993.10476347
出版商: Taylor & Francis Group
关键词: Density estimation;Loess;Nonhomogeneous Poisson processes;Spectrum estimation
数据来源: Taylor
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 localaveraging(the“A”in ATS) is carried out, then a variance-stabilizingtransformationis applied (“T”), and finally the result issmoothed(“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.
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