Y. Wang, "Function estimation via Wavelets for data with long-range dependence," Technical Report, Dept. of Statistics, University of Missouri-Columbia, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....( 12] 13] Thresholding is a nonlinear technique, yet it is very simple because it operates on one wavelet coefficient at a time. Alternative approaches to non linear wavelet based denoising can be found in, for example, 1] 3] 7] 9] 8] 11] 17] 18] 23] 26] 27] 28] 31] [32], 35] and references therein. On a seemingly unrelated front, lossy compression has been proposed for denoising in several CHANG, YU, AND VETTERLI 3 works [4] 5] 20] 24] 27] Concerns regarding the compression rate were explicitly addressed. This is important because any practical ....

Y. Wang, "Function estimation via Wavelets for data with long-range dependence," Technical Report, Dept. of Statistics, University of Missouri-Columbia, 1994.

....work on operator biorthogonal decompositions. Lemari e [27] and Daubechies [9] have developed biorthogonal decompositions of the differentiation operator. Jawerth and Sweldens [24] 55] have decomposed variablecoefficient differential operators using biorthogonal decompositions. Also, Y. Wang [58] uses the WVD as a theoretical tool to decorrelate fractional Gaussian noise in function estimation problems from data with long range dependence. In the area of astronomy, Starck et al. 51] 52] 53] 54] have developed methods for image reconstruction in aperture synthesis and deconvolution ....

Y. Wang (1994) Function Estimation via Wavelets for Data with Long-Range Dependence. Technical Report, Department of Statistics, University of MissouriColumbia.

....As before, one could use these last to develop estimates of f (0) and var h (x ) Rosenblatt [40] also develops such large sample distributions in the long memory case. Robinson [39] reviews the kernel estimation of mean level functions in the presence of long memory noise. Wang [51] considers efficiency results. Percival and Guttorp [38] employ the Haar transform to study the Hurst effect. Gao [23] considers the L 2 norm under a long memory assumption. 8. SHRINKAGE ESTIMATES By shrinkage is here meant the replacement of the sample coefficients of a statistic by related ....

Y. Wang, Function estimation via wavelets for data with long-range dependence, preprint, University of Missouri-Columbia, 1994.

.... (representing significant signal ) and small coefficients (corresponding to random noise ) It should be noted that other data dependent thresholding techniques (e.g. Donoho and Johnstone (1995a) Nason (1994) Weyrich and Warhola (1994) Ogden and Parzen (1994) Vidakovic (1994) Wang (1994) and others) consider only the magnitude of the empirical coefficients. In addition to the relative magnitude of coefficients, it is desirable also to include the information contained in the position of the large coefficients. To illustrate the importance of this consideration, Figure 1 is a plot ....

Wang, Y. (1994). Function estimation via wavelets for data with longrange dependence. Technical report, University of Missouri Department of Statistics, Columbia, Missouri 65211.

....to yield an estimate of f . See Donoho and Johnstone [DJ2, DJ1] Donoho, Johnstone, Kerkyacharian and Picard [DJKP] Nason [Na2, Na3] Abramovich and Benjamini [AB1] Fan, Hall, Martin and Patil [FHMP] Johnstone and Silverman [JS] Neumann and Spokoiny [NS2] Ogden [Og1] Vidakovic [Vi1] Wang [Wa1] and Weyrich and Warhola [WW1] In this paper we only mention regression in passing, but note that the two inverse methods set out in the above section yield two contrasting approaches to the nonparametric regression problem. Once the stationary wavelet transform of the data has been worked out, ....

Wang, Y.: Function estimation via wavelets for data with long-range dependence. Technical Report, Univeristy of Missouri, Columbia, (1994).

....[3] 16] 19] 23] Our threshold is also estimated from the Bayesian setting, using the Generalized Gaussian distribution under the soft threshold rule, similar to the framework in [16] for hard thresholding. For non parametric methods, a popular approach is to use cross validation [9] 13] [24], 25] Saito [17] proposed an interesting approach of incorporating MDL into the hard thresholding rule to achieve simultaneous denoising and compression, where the MDL criterion determines the subset of coefficients to retain. This approach is relevant to our quantization method and will be ....

Y. Wang, "Function estimation via Wavelets for data with long-range dependence," Technical Report, Dept. of Statistics, University of Missouri-Columbia, 1994.

No context found.

Y. Wang, "Function estimation via Wavelets for data with long-range dependence," Technical Report, Dept. of Statistics, University of Missouri-Columbia, 1994.

No context found.

Wang, Y.: Function estimation via wavelets for data with long-range dependence. Technical Report, Univeristy of Missouri, Columbia, (1994).

No context found.

Soc., 28, 288--305. Wang, Y. (1994) Function estimation via wavelets for data with long-range dependence. Technical Report. Department of Statistics, University of Missouri, Columbia.

No context found.

Wang, Y. (1994). Function estimation via wavelets for data with long-range dependence. Tech. Report. Department of Statistics, University of Missouri-Columbia.

No context found.

Soc., 28, 288--305. Wang, Y. (1994) Function estimation via wavelets for data with long-range dependence. Technical Report. Department of Statistics, University of Missouri, Columbia.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC