JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Note on Nonparametric Density Estimation for the Deconvolution Problem
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Note on Nonparametric Density Estimation for the Deconvolution Problem
Lee, Sung-Ho;
  PDF(new window)
 Abstract
In this paper the support vector method is presented for the probability density function estimation when the sample observations are contaminated with random noise. The performance of the procedure is compared to kernel density estimates by the simulation study.
 Keywords
Nonparametric density estimation;deconvolution;kernel estimator;support vector;reproducing kernel Hilbert space(RKHS);
 Language
English
 Cited by
1.
영상흐림보정에서 EM 알고리즘의 일반해: 반복과정을 사용하지 않는 영상복원,김승구;

Communications for Statistical Applications and Methods, 2009. vol.16. 3, pp.409-419 crossref(new window)
2.
A Support Vector Method for the Deconvolution Problem,;

Communications for Statistical Applications and Methods, 2010. vol.17. 3, pp.451-457 crossref(new window)
 References
1.
Aronszajn, N. (1950). Theory of reproducing kernels, Transactions of the American Mathematical Society, 68, 337-404 crossref(new window)

2.
Carroll, R. J. and Hall, P. (1988). Optimal rates of convergence for deconvoluting a density, Journal of the American Statistical Association, 83, 1184-1886 crossref(new window)

3.
Fan, J. (1991). On the optimal rates of convergence for nonparametric deconvolution problems, The Annals of Statistics, 19, 1257-1272 crossref(new window)

4.
Gunn, S. R. (1998). Support vector machines for classi¯cation and regression. Technical report. University of Southampton

5.
Lee, S. (2001). A note on strongly consistent wavelet density estimator for the deconvolution problem, The Korean Communications in Statistics, 8, 859-866

6.
Lee, S. (2002). A note on Central limit theorem for deconvolution wavelet density estimator, The Korean Communications in Statistics, 9, 241-248 crossref(new window)

7.
Lee, S. and Hong, D. H. (2002). On a strongly consistent wavelet density estimator for the deconvolution problem, Communications in Statistics - Theory and Methods, 31, 1259-1272 crossref(new window)

8.
Lee, S. and Taylor, R. L. (2008). A note on support vector density estimation for the deconvolution problem, Communications in Statistics - Theory and Methods, 37, 328-336 crossref(new window)

9.
Liu, M. C. and Taylor, R. L. (1989). A consistent nonparametric density estimator for the deconvolution problem, The Canadian Journal of Statistics, 17, 427-438 crossref(new window)

10.
Louis, T. A. (1991). Using empirical Bayes methods in biopharmaceutical research, Statistics in Medicine, 10, 811-827 crossref(new window)

11.
Mukherjee, S. and Vapnik, V. (1999). Support vector method for multivariate density estimation. Technical Report. A.I. Memo no. 1653, MIT AI Lab

12.
Nadaraya, E. (1964). On regression estimators, Theory of Probability and It's Application, 9, 157-159

13.
Pensky, M. and Vidakovic, B. (1999). Adaptive wavelet estimator for nonparametric density deconvolutoin, The Annals of Statistics, 27, 2033-2053 crossref(new window)

14.
Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer Verlag, New York

15.
Vapnik, V. and Chervonenkis, A. (1964). A note on one class of perceptrons, Automation and Remote Control, 25

16.
Vapnik, V. and Lerner, A. (1963). Pattern recognition using generalized portrait method, Automation and Remote Control, 24

17.
Walter, G. G. (1999). Density estimation in the presence of noise, Statistics & Probability Letters, 41, 237-246 crossref(new window)

18.
Watson, G. S. (1964). Smooth regression analysis, Sankhya: The Indian Journal of Statistics, Ser. A, 26, 359-372

19.
Weston, J., Gammerman, A., Stitson, M., Vapnik, V., Vovk, V. and Watkins, C. (1999). Support vector density estimation, In Scholkopf, B. and Smola, A., editors, Advances in Kernel Methods-Suppot Vector Learning, 293-306, MIT Press, Cambridge, MA

20.
Zhang, H. P. (1992). On deconvolution using time of flight information in positron emission tomography, Statistica Sinica, 2, 553-575