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Comparison of Jump-Preserving Smoothing and Smoothing Based on Jump Detector
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 Title & Authors
Comparison of Jump-Preserving Smoothing and Smoothing Based on Jump Detector
Park, Dong-Ryeon;
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 Abstract
This paper deals with nonparametric estimation of discontinuous regression curve. Quite number of researches about this topic have been done. These researches are classified into two categories, the indirect approach and direct approach. The major goal of the indirect approach is to obtain good estimates of jump locations, whereas the major goal of the direct approach is to obtain overall good estimate of the regression curve. Thus it seems that two approaches are quite different in nature, so people say that the comparison of two approaches does not make much sense. Therefore, a thorough comparison of them is lacking. However, even though the main issue of the indirect approach is the estimation of jump locations, it is too obvious that we have an estimate of regression curve as the subsidiary result. The point is whether the subsidiary result of the indirect approach is as good as the main result of the direct approach. The performance of two approaches is compared through a simulation study and it turns out that the indirect approach is a very competitive tool for estimating discontinuous regression curve itself.
 Keywords
Difference Kernel estimators;discontinuous regression function;lump detector;jump-preserving smoothing;local constant M-smoother;
 Language
English
 Cited by
1.
Comparison of Nonparametric Function Estimation Methods for Discontinuous Regression Functions,;

응용통계연구, 2010. vol.23. 6, pp.1245-1253 crossref(new window)
2.
Bootstrap Bandwidth Selection Methods for Local Linear Jump Detector,;

Communications for Statistical Applications and Methods, 2012. vol.19. 4, pp.579-590 crossref(new window)
1.
Comparison of Nonparametric Function Estimation Methods for Discontinuous Regression Functions, Korean Journal of Applied Statistics, 2010, 23, 6, 1245  crossref(new windwow)
2.
Bootstrap Bandwidth Selection Methods for Local Linear Jump Detector, Communications for Statistical Applications and Methods, 2012, 19, 4, 579  crossref(new windwow)
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