The maximum likelihood estimator (MLE) has commonly been used to estimate the unknown
parameters in a finite mixture of distributions. However, the MLE can be very sensitive to …

In the present paper we propose a novel method for the identification and modeling of
neural networks using extracellular spike recordings. We create a deterministic model of the …

The observed discharge, an important input for flood forecasting systems, can significantly
affect the accuracy of forecasting results. Since the reservoir inflow is not measured directly …

this paper, we present a new automatic robust algorithm to segment multimodal brain MR
images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using …

Generalized d-fullness Technique for Breakdown Point Study of the Trimmed Likelihood
Estimator with Application Page 1 Statistics for Industry and Technology, 83-91 Đ 2004 …

The least squares estimator is quite sensitive to data contamination and model
misspecification. This sensitivity is addressed by the theory of robust statistics which builds …

High breakdown-point regression estimators protect against large errors both in explanatory
and dependent variables. The least trimmed squares (LTS) estimator is one of frequently …

The Maximum Likelihood Estimator (MLE) has been widely used to estimate the unknown
parameters in the finite mixture of Generalized Linear Models (GLMs). However, the MLE …

In regression analysis least square (LS) estimator fails because of its sensitivity to unusual
observations present in the data. Robust estimation provides alternative estimates which are …

L'imagerie médicale, en constante évolution ces dernieres années, fournit un nombre
croissant de données. Ce volume important de données doit ensuite être analysé. Les …