Wavelet Thresholding Risk Estimate for the Model with Random Samples and Correlated Noise

Abstract

Signal de-noising methods based on threshold processing of wavelet decomposition coefficients have become popular due to their simplicity, speed, and ability to adapt to signal functions with spatially inhomogeneous smoothness. The analysis of the errors of these methods is an important practical task, since it makes it possible to evaluate the quality of both methods and equipment used for processing. Sometimes the nature of the signal is such that its samples are recorded at random times. If the sample points form a variational series based on a sample from the uniform distribution on the data registration interval, then the use of the standard threshold processing procedure is adequate. The paper considers a model of a signal that is registered at random times and contains noise with long-term dependence. The asymptotic normality and strong consistency properties of the mean-square thresholding risk estimator are proved. The obtained results make it possible to construct asymptotic confidence intervals for threshold processing errors using only the observed data.

Method of Constructing a Nonlinear Approximating Scheme of a Complex Signal: Application Pattern Recognition

“…Currently, scientists are actively conducting research related to the development of methods for modeling and analyzing complex nonstationary signals [1][2][3]. The need to create such methods arises when carrying out a number of fundamental and applied investigations in such areas as biomedicine, geophysics, ecology, seismology, etc.…”