Robust local parameter estimator based on least absolute value estimator

Weighted Least Absolute Deviation Lasso Estimator

The linear absolute shrinkage and selection operator(Lasso) method improves the low prediction accuracy and poor interpretation of the ordinary least squares(OLS) estimate through the use of L1 regularization on the regression coefficients. However, the Lasso is not robust to outliers, because the Lasso method minimizes the sum of squared residual errors. Even though the least absolute deviatio...

Level Robust Methods Based on the Least Squares Regression Estimator

Robust learning algorithm based on LTA estimator

Gross errors and outliers in the feedforward neural networks training sets may often corrupt the performance of traditional learning algorithms. Such algorithms try to fit networks to the contaminated data, so the resulting model may be far from the desired one. In this paper we propose new, robust to outliers, learning algorithm based on the concept of the least trimmed absolute value (LTA) es...

A novel robust position estimator for self-sensing magnetic levitation systems based on least squares identification

In this work a novel method is introduced for the estimation of the position of a self-sensing magnetic levitation system, based on a least squares identification strategy. In the first step, a detailed mathematical model of the magnetic levitation system is derived and the properties of this system are analyzed for the case of a pulse-width modulated control. Based on this model, an estimation...

An Entropy-Like Estimator for Robust Parameter Identification

This paper describes the basic ideas behind a novel prediction error parameter identification algorithm exhibiting high robustness with respect to outlying data. Given the low sensitivity to outliers, these can be more easily identified by analysing the residuals of the fit. The devised cost function is inspired by the definition of entropy, although the method in itself does not exploit the st...