Abstract We address the Least Quantile of Squares (LQS)(and in particular the Least Median
of Squares) regression problem using modern optimization methods. We propose a Mixed …

Learning robust regression model from high-dimensional corrupted data is an essential and
difficult problem in many practical applications. The state-of-the-art methods have studied …

Highlights•First research on the estimating diffusion coefficient of ozone gas in rice
grains.•CFD allows understanding the ozone gas flow in rice grains as a porous …

In this latest work, the Newly Modified Robust Tensor Principal Component Analysis (New
RTPCA) using affine transformation and L2, 1 norms is proposed to remove the outliers and …

OSIRIS is a mathematically-based software tool for Short Tandem Repeat (STR) and DNA
fragment analysis (https://www. ncbi. nlm. nih. gov/osiris/). As part of its routine sample …

For estimating the parameters of models for financial market data, the use of robust
techniques is of particular interest. Conditional forecasts, based on the capital asset pricing …

In this work, we propose a novel method for head pose estimation and face recovery,
particularly to solve the potential impacts of noises in signal processing to get an efficient …

Given a realization of n observations in X, Y pairs, called the sample, the goal is to estimate
the parameters, b 0 and b 1. This is usually done by fitting a line to the observations in the …

Ordinary Least squares (OLS) are the most widely used due to tradition and their optimal
properties to estimate the parameters of linear and nonlinear regression models …

Least squares (LS) with Gauss-Newton method is the most widely used approach to
estimate the parameters of nonlinear regression models. In the presence of outliers, even …