The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable
selection represents a successful methodology for dimensionality reduction, which is …

In this paper, a generalized nonlinear multi-regression model based on Choquet integral
(NMRCI) is proposed and applied for estimation of non-additive systems that include outliers …

Latent time series models such as (the independent sum of) ARMA (p, q) models with
additional stochastic processes are increasingly used for data analysis in biology, ecology …

Regression models explore relationship between a response variable and some
explanatory variables based often on conditionally mean function. The choice of mean …

This work studies implicitly weighted robust statistical methods suitable for econometric
problems. We study robust estimation mainly for the context of heteroscedasticity or high …

A robust procedure is proposed, starting from least trimmed squares as the initial estimator.
The asymptotic distribution of the two-step and multi-step estimators is derived. This allows …

High breakdown-point regression estimators protect against large errors and data
contamination. We adapt and generalize the concept of trimming used by many of these …

This paper shows the project risk management methods, which allow to better identify risks
in the construction of high-rise buildings and to manage them throughout the life cycle of the …

Robust statistical procedures are" close" to the optimal parametric procedures when the real
distribution coincides with the known one and stably retains its qualities as long as the true …

We present a new framework for robust estimation and inference on second-order stationary
time series and random fields. This framework is based on the Generalized Method of …