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Titlebook: Wavelets and Statistics; Anestis Antoniadis,Georges Oppenheim Book 1995 Springer-Verlag New York 1995 Gaussian process.Hypothese.Markov ra

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61#
發(fā)表于 2025-4-1 05:53:37 | 只看該作者
Discretized Wavelet Density Estimators for Continuous Time Stochastic Processes,ch are satisfied for rather general diffusion processes, the. . error of the linear wavelet estimator of. constructed from the observation . converges with the rate . when . In this work we study two discretized versions of this estimator, constructed from the dicrete observations . We show that the
62#
發(fā)表于 2025-4-1 07:24:47 | 只看該作者
63#
發(fā)表于 2025-4-1 10:47:15 | 只看該作者
64#
發(fā)表于 2025-4-1 16:51:50 | 只看該作者
65#
發(fā)表于 2025-4-1 20:57:01 | 只看該作者
66#
發(fā)表于 2025-4-2 02:42:19 | 只看該作者
Choice of the Threshold Parameter in Wavelet Function Estimation, data. The choice of threshold is crucial to the success of the method and is currently subject to an intense research effort. We describe how we have applied the statistical technique of cross-validation to choose a threshold and we present results that indicate that its performance for correlated
67#
發(fā)表于 2025-4-2 03:29:02 | 只看該作者
Choice of the Threshold Parameter in Wavelet Function Estimation, data. The choice of threshold is crucial to the success of the method and is currently subject to an intense research effort. We describe how we have applied the statistical technique of cross-validation to choose a threshold and we present results that indicate that its performance for correlated
68#
發(fā)表于 2025-4-2 08:23:43 | 只看該作者
The Stationary Wavelet Transform and some Statistical Applications,useful subsequently in the paper. A ‘stationary wavelet transform’, where the coefficient sequences are not decimated at each stage, is described. Two different approaches to the construction of an inverse of the stationary wavelet transform are set out. The application of the stationary wavelet tra
69#
發(fā)表于 2025-4-2 11:57:17 | 只看該作者
70#
發(fā)表于 2025-4-2 18:05:01 | 只看該作者
Wavelet Thresholding: Beyond the Gaussian I.I.D. Situation, of these applications are based on. of the empirical coefficients. For regression and density estimation with independent observations, we establish joint asymptotic normality of the empirical coefficients by means of strong approximations. Then we describe how one can prove asymptotic normality un
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