JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Estimation of Jump Points in Nonparametric Regression
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Estimation of Jump Points in Nonparametric Regression
Park, Dong-Ryeon;
  PDF(new window)
 Abstract
If the regression function has jump points, nonparametric estimation method based on local smoothing is not statistically consistent. Therefore, when we estimate regression function, it is quite important to know whether it is reasonable to assume that regression function is continuous. If the regression function appears to have jump points, then we should estimate first the location of jump points. In this paper, we propose a procedure which can do both the testing hypothesis of discontinuity of regression function and the estimation of the number and the location of jump points simultaneously. The performance of the proposed method is evaluated through a simulation study. We also apply the procedure to real data sets as examples.
 Keywords
Discontinuous regression function;jump detection;nonparametric regression;
 Language
English
 Cited by
1.
Comparison of Jump-Preserving Smoothing and Smoothing Based on Jump Detector,;

Communications for Statistical Applications and Methods, 2009. vol.16. 3, pp.519-528 crossref(new window)
2.
Bandwidth Selection for Local Smoothing Jump Detector,;

Communications for Statistical Applications and Methods, 2009. vol.16. 6, pp.1047-1054 crossref(new window)
3.
Comparison of Nonparametric Function Estimation Methods for Discontinuous Regression Functions,;

응용통계연구, 2010. vol.23. 6, pp.1245-1253 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)
 References
1.
Bowman, A. W., Pope, A. and Ismail, B. (2006). Detecting discontinuities in nonparametric regression curves and surfaces, Statistics & Computing, 16, 377-390 crossref(new window)

2.
Gijbels, I. and Goderniaux, A. C. (2004). Bandwidth selection for change point estimation in nonparametric regression, Technometrics, 46, 76-86 crossref(new window)

3.
Hall, P. and Titterington, D. M. (1992). Edge-preserving and peak-preserving smoothing, Technometics, 34, 429-440 crossref(new window)

4.
Muller, H. G. (1992). Change-points in nonparametric regression analysis, The Annals of Statistics, 20, 737-761 crossref(new window)

5.
Muller, H. G. and Stadtmuller, U. (1999). Discontinuous versus smooth regression, The Annals of Statistics, 27, 299-337 crossref(new window)

6.
Park, D. (2008). Test for discontinuities in nonparametric regression, To appear in Communications of the Korean Statistical Society

7.
Qiu, P. (1994). Estimation of the number of jumps of the jump regression functions, Communications in Statistics-Theory and Methods, 23, 2141-2155 crossref(new window)

8.
Qiu, P. (2005). Image Processing and Jump Regression Analysis, John Wiley & Sons, New Jersey

9.
Qiu, P., Asano, Chi. and Li, X. (1991). Estimation of jump regression functions, Bulletin of Informatics and Cybernetics, 24, 197-212

10.
Qiu, P. and Yandell, B. (1998). A local polynomial jump detection algorithm in nonparametric regression, Technometrics, 40, 141-152 crossref(new window)

11.
Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice and Visualization, Wiley-Interscience, New York

12.
Wu, J. S. and Chu, C. K. (1993). Kernel-type estimators of jump points and values of a regression function, The Annals of Statistics, 21, 1545-1566 crossref(new window)