Supplemental Material for ”Heavy Tail Robust Frequency Domain Estimation”

Heavy-Tail and Plug-In Robust Consistent Conditional Moment Tests of Functional Form: Supplemental Appendix

Robust Space/Spatial-Frequency based Filtering of Images in the presence of heavy tailed noise

A robust space/spatial-frequency representation for analysis of two-dimensional signals corrupted by heavy tailed noise is proposed. The L-estimate forms of robust short-time Fourier transform and spectrogram for 2D signals are introduced. They are used to define a non-stationary space-varying filtering procedure in the space/spatial-frequency domain. The efficiency of the proposed procedure is...

Filtering of Frequency Modulated Signals in Impulse Noise Environments Based on Robust Dft Forms

A strategy for filtering (or denoising) of high-pass signals embedded in an impulse (heavy tail) noise environments based on the robust DFT forms has been proposed recently. In this paper we investigate various approaches for design of the robust DFT used in this application. We consider the DFT estimates based on the: linear combination of order statistics (L-estimate), myriad, and Wilcoxon es...

Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...

Least Tail-Trimmed Squares for In...nite Variance Autoregressions

We develop a robust least squares estimator for autoregressions with possibly heavy tailed errors. Robustness to heavy tails is ensured by negligibly trimming the squared error according to extreme values of the error and regressors. Tail-trimming ensures asymptotic normality and superp -convergence with a rate comparable to the highest achieved amongst M-estimators for stationary data. Moreov...