Self-Selecting Robust Logistic Regression Model

Robust testing in the logistic regression model

Distributionally Robust Logistic Regression

This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains the unknown datagenerating distribution with high confidence. We then formulat...

Robust Logistic Regression and Classification

We consider logistic regression with arbitrary outliers in the covariate matrix. We propose a new robust logistic regression algorithm, called RoLR, that estimates the parameter through a simple linear programming procedure. We prove that RoLR is robust to a constant fraction of adversarial outliers. To the best of our knowledge, this is the first result on estimating logistic regression model ...

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 ...

In Logistic Regression Model

Logistic regression with binary and multinomial outcomes is commonly used, and researchers have long searched for an interpretable measure of the strength of a particular logistic model. This article describes the large sample properties of some pseudo-R statistics for assessing the predictive strength of the logistic regression model. We present theoretical results regarding the convergence an...