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In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here...
In this paper, a hard thresholding wavelet estimator is constructed for a deconvolution model in a periodic setting that has long-range dependent noise. The estimation paradigm is based on a maxiset m...
We investigate the estimation of a weighted density taking the formg=w(F)f, where fdenotes an unknown density,Fthe associated distribution function andwis a known (non-negative) weight.Such a class en...
The Mat\'ern covariance function is a popular choice for modeling dependence in spatial environmental data.
The danger of confusing long-range dependence with non-stationarity has been pointed out by many authors.
We consider stationary processes with long memory which are non–Gaussian and represented as Hermite polynomials of a Gaussian process. We focus on the corresponding wavelet coefficients and study th...
This paper is first devoted to study an adaptive wavelet based estimator of the long memory parameter for linear processes in a general semi-parametric frame. This is an extension of Bardet {\it et al...
We consider perfect simulation algorithms for locally stable point processes based on dominated coupling from the past, and apply these methods in two different contexts. A new version of the algori...
This work provides an application of wavelet analysis to pricing and hedging path-dependent contingent claims within the framework of the Black-Scholes model.
We consider the regression model with (known) random design. We investigate the minimax performances of an adaptive wavelet block thresholding estimator under the Lp risk with p >2 over Besov balls. W...
Wavelet Scalograms and their Applications in Economic Time Series。
Probability Density Functions of the Empirical Wavelet Coefficients of a Wavelet Estimator of Multidimensional Poisson Intensities。
We propose a wavelet-based technique for the nonparametric estimation of functions contaminated with noise whose mean and variance are linked via a possibly unknown variance function. Our method, term...
A data-driven block thresholding procedure for wavelet regression is proposed and its theoretical and numerical properties are investigated. The procedure empirically chooses the block size and thre...
We introduce a new method of Bayesian wavelet shrinkage for recon- structing a signal when we observe a noisy version. Rather than making the usual assumption that the wavelet coecients of the sign...

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