Robust Estimation in the Linear Model

Robust Estimation in Linear Regression Model: the Density Power Divergence Approach

The minimum density power divergence method provides a robust estimate in the face of a situation where the dataset includes a number of outlier data. In this study, we introduce and use a robust minimum density power divergence estimator to estimate the parameters of the linear regression model and then with some numerical examples of linear regression model, we show the robustness of this est...

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Robust maximum likelihood estimation in the linear model

This paper addresses the problem of maximum likelihood parameter estimation in linear models a!ected by Gaussian noise, whose mean and covariance matrix are uncertain. The proposed estimate maximizes a lower bound on the worst-case (with respect to the uncertainty) likelihood of the measured sample, and is computed solving a semide"nite optimization problem (SDP). The problem of linear robust e...

Robust Estimation in the Heteroscedastic Linear

We study estiuultion of regression parameters in heteroscedastic linear models when the ntunber of parameters is large. The results

Comparing The ANN And Linear Regression In Estimation Of The Growth Model (The Case Of MENA)