具有长程相关误差的周期性小波反褶积
Wavelet Deconvolution in a Periodic Setting with Long-Range Dependent Errors
摘要：
在本文中，硬阈值小波估计器构造的反褶积模型在一个周期设置，具有长程相关的噪声。该方法基于极大集方法，在强相关的情况下，对各种L_p损失函数和各种Besov空间都能获得接近最优的收敛速度.长程相关的影响不利于收敛速度。该方法是使用修改的WaveD-包在R和进行了广泛的数值研究。数值研究补充了理论结果，并比较了LRD估计与单纯使用标准WaveD方法。
英文摘要：
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 method that attains a near optimal rate of convergence for a variety of L_p loss functions and a wide variety of Besov spaces in the presence of strong dependence. The effect of long-range dependence is detrimental to the rate of convergence. The method is implemented using a modification of the WaveD-package in R and an extensive numerical study is conducted. The numerical study supplements the theoretical results and compares the LRD estimator with naïvely using the standard WaveD approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
