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SiZer for Exploration of Structures in Curves

 

作者: Probal Chaudhuri,   J.S. Marron,  

 

期刊: Journal of the American Statistical Association  （Taylor Available online 1999）
卷期: Volume 94, issue 447  

页码: 807-823

 

ISSN:0162-1459

 

年代: 1999

 

DOI:10.1080/01621459.1999.10474186

 

出版商: Taylor & Francis Group

 

关键词: Confidence bands;Curve estimation;Kernel estimates;Local polynomials;Nonparametric smoothing;Scale space;Significant features;SiZer map

 

数据来源: Taylor

 

摘要:

In the use of smoothing methods in data analysis, an important question is which observed features are “really there,” as opposed to being spurious sampling artifacts. An approach is described based on scale-space ideas originally developed in the computer vision literature. Assessment of Significant ZERo crossings of derivatives results in the SiZer map, a graphical device for display of significance of features with respect to both location and scale. Here “scale” means “level of resolution”; that is, “bandwidth.”

 

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