Nonparametric regression models offer a way to understand and quantify relationships
between variables without having to identify an appropriate family of possible regression …

Physicians caring for chronically ill individuals need to predict patients' disease progression,
as accurate disease projections can facilitate better treatment decisions. The power of …

Sparse domain thresholding filters operating in a sparse domain are highly effective in
removing Gaussian random noise under Gaussian distribution assumption. Erratic noise …

Radio frequency interference (RFI) mitigation for pulsar signals is a long perplexing issue in
astrophysical measurements. Linear mitigation methods are often criticized for limited RFI …

Erratic noise often has high amplitudes and a non‐Gaussian distribution. Least‐squares–
based approaches therefore are not optimal. This can be handled better with non–least …

An examination of mathematical formulations of ridge-regression-type estimators points to a
curious observation: estimators can be derived by both Bayesian and Frequentist methods …

Seismic denoising will inevitably cause signal leakage, which is seen as coherent signal
energy in the removed noise profile. Most traditional methods either ignore the signal …

This paper considers the problem of matrix completion when the observed entries are noisy
and contain outliers. It begins with introducing a new optimization criterion for which the …

Quantile regression investigates the conditional quantile functions of a response variable in
terms of a set of covariates. M-quantile regression extends this idea by a 'quantile …

In this paper we review existing methods for robust functional principal component analysis
(FPCA) and propose a new method for FPCA that can be applied to longitudinal data where …