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Proceedings Paper

The application of Taylor expansion to error density estimation nonparametric regression
Author(s): Qingjian Zeng; Qing Li
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Paper Abstract

In this paper, by applying the Taylor expansion, the authors study the asymptotic properties of the kernel density estimation fn(e) of an unknown error distribution function f(e) in a nonparametric regression model. Then, they study the choice of the smoothing parameters in the estimation fn(e). Finally, an approximation confidence interval of f(e) was given.

Paper Details

Date Published: 8 June 2012
PDF: 5 pages
Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 833423 (8 June 2012); doi: 10.1117/12.954151
Show Author Affiliations
Qingjian Zeng, Guangdong Songshan Polytechnic College (China)
Qing Li, Guangdong Songshan Polytechnic College (China)


Published in SPIE Proceedings Vol. 8334:
Fourth International Conference on Digital Image Processing (ICDIP 2012)
Mohamed Othman; Sukumar Senthilkumar; Xie Yi, Editor(s)

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