Models of Noise and Robust Estimation

This paper shows that using robust least squares estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the estimator.

Abstract

Given n noisy observations gi of the same quantity f, it is common use to give an estimate of f by minimizing the function sum n sub i = 1(gi - f) 2. From a statistical point of view this corresponds to computing the Maximum Likelihood estimate, under the assumption of Gaussian noise. However, it is well known that this choice leads to results that are very sensitive to the presence of outliers in the data. For this reason it has been proposed to minimize functions of the form sum n sub i = 1(gi-f), where V is a function that increases less rapidly than the square. Several choices for V have been proposed and successfully used to obtain 'robust' estimates. In this paper we show that, for a class of functions V, using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the shape of V.... Robust estimation, Noise, Outliers.