Kovac, A. (1999). Wavelet Thresholding for Unequally Spaced Data. PhD thesis, University of Bristol, Bristol. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Wavelet Shrinkage of Itch Response Data - Morgan, Nason (Correct)
.... may be appropriate to extend our analyses to to incorporate Poisson like error distributions and also more advanced thresholding techniques such as: choosing different thresholds for different resolution levels (for correlated data) see Johnstone and Silverman [8] applying the methods of Kovac [10] which extend the wavelet shrinkage method to datasets with arbitrary design and number of points whilst retaining the speed of computation. 7 Acknowledgments We would like to thank Francis McGlone for supplying the data used in this article. We would like to thank three referees and the ....
A. Kovac. Wavelet thresholding for unequally spaced data. PhD thesis, Department of Mathematics, University of Bristol, Bristol, UK, 1998.
Statistical Modelling of Time Series Using.. - Nason, Sapatinas.. (1998) (Correct)
....a finest resolution level J is chosen and the coefficients, c J , are computed from the data, fX t g N t=1 , at this finest level. In general this can be done using interpolation or projection methods such as direct computation as in (3) or see Donoho (1992) Delyon and Juditsky (1995) and Kovac (1997). If there are N = 2 J (for some integer J) equally spaced data points then the finest coefficients can be obtained approximately by just setting them to be equal to the data. In other words c J n = Xn ; for n = 1; N : 7) In all cases described below the smooth at level J , c J , ....
Kovac, A. (1997). Wavelet thresholding for unequally spaced data. Ph.D. thesis, Department of Mathematics, University of Bristol, Bristol.
Statistical Modelling of Time Series Using.. - Nason, Sapatinas.. (1998) (Correct)
....a finest resolution level J is chosen and the coefficients, c J , are computed from the data, X t N t=1 , at this finest level. In general this can be done with interpolation or projection methods such as direct computation as in (4) or see Donoho (1992) Delyon and Juditsky (1995) and Kovac (1997). If there are N = 2 J (for some integer J) equally spaced data points then the finest coefficients can be obtained approximately by just setting them to be equal to the data. In other words c J n = Xn , for n = 1, N. 8) In all cases described below the smooth at level J, c J , ....
Kovac, A. (1997). Wavelet thresholding for unequally spaced data. Ph.D. thesis, Department of Mathematics, University of Bristol, Bristol.
Wavelet Shrinkage of Itch Response Data - Morgan, Nason (1998) (Correct)
....will be just as, if not more, successful. In the future we would like to incorporate Poisson error distributions and also more advanced thresholding techniques such as: choosing different thresholds for different resolution levels, see Johnstone and Silverman [8] applying the methods of Kovac [10] which extend the wavelet shrinkage method to datasets with arbitrary design and number of points whilst retaining the speed of computation. 7 Acknowledgments We would like to thank Francis McGlone for supplying the data used in this article. We would like to thank Anestis Antoniadis for ....
A. Kovac. Wavelet thresholding for unequally spaced data. PhD thesis, Department of Mathematics, University of Bristol, Bristol, UK, 1998.
Extending The Scope Of Wavelet Regression Methods By - Coefficient-Dependent.. Self-citation (Kovac) (Correct)
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Kovac, A. (1999). Wavelet Thresholding for Unequally Spaced Data. PhD thesis, University of Bristol, Bristol.
Extending The Scope Of Wavelet Regression Methods By.. - Kovac, Silverman (1998) (15 citations) Self-citation (Kovac) (Correct)
...., wk 2 2 = J,1 X j=j0 2 j X k=0 # # S #fWR#f # #g jk ;# jk # , w jk # 2 where # jk are the individual thresholds, and j 0 is a cut off level , below which no thresholding is carried out. We consider here soft thresholding only. Hard thresholding can be analyzed similarly (Kovac, 1999). Let w # be the DWT of the sequence Rf # . The individual coefficient #WR#f # ## jk is normally distributed with mean w # jk and variance # jk that can be calculated with the algorithms introduced above. To explore the mean square error, we define ### ; # 1 ;# 2 ;##=E ## 1 , # S #X; ....
Kovac, A. (1999). Wavelet Thresholding for Unequally Spaced Data. PhD thesis, University of Bristol, Bristol.
Nonlinear Orthogonal Series Estimates For Random Design Regression - Kohler (1998) (Correct)
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Kovac, A. and Silverman, B.W. (1998). Wavelet thresholding for unequally spaced data. Submitted for publication.
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