Robustness against separation and outliers in logistic regression

Some New Estimation Methods for Weighted Regression When There Are Possible Outliers

The problem of estimating the variance parameter robustly in a heteroscedatic linear model is considered. The situation where the variance is a function of the explanatory variables is treated. To estimate the variance robustly in this case, it is necessary to guard against the influence of outliers in the design as well as outliers in the response. By analogy with the homoscedastic regression ...

Comparison of ordinary logistic regression and robust logistic regression models in modeling of pre-diabetes risk factors

Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over ...

Using Classic Discriminant Analysis and Detection Function for Separation of Chemical Victims in Sardasht City to Exposed and Non Exposure Mustard Groups in 2013 and Comparison with Logistic Regression

Robust and Sparse Regression via γ-Divergence

In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been studied for robust parameter estimation, and the corresponding divergences have been discussed. One such divergence is the γ-divergence, and the robust estimator using the γ-divergence is known for having a strong robus...

Robustness of reweighted Least Squares Kernel Based Regression

Kernel Based Regression (KBR) minimizes a convex risk over a possibly infinite dimensional reproducing kernel Hilbert space. Recently it was shown that KBR with a least squares loss function may have some undesirable properties from a robustness point of view: even very small amounts of outliers can dramatically affect the estimates. KBR with other loss functions is more robust, but often gives...