Binary logistic regression is a versatile statistical tool for analyzing binary responses in a wide range of disciplines (see Statnews #44 for an introduction to logistic regression). Similar to ordinary least squares regression, one key assumption in regular binary logistic regression is that observations are independent of each other. Violations of the assumption of independence of observatio...

We consider univariate regression estimation from an individual (non-random) sequence (x1, y1), (x2, y2), . . . ∈ IR×IR, which is stable in the sense that for each interval A ⊆ IR, (i) the limiting relative frequency of A under x1, x2, . . . is governed by an unknown probability distribution μ, and (ii) the limiting average of those yi with xi ∈ A is governed by an unknown regression function m...

Logistic multiple regression using the method of maximum likelihood is now the method of choice for many regression-type problems involving binary, ordinal. or nominal dependent variables. Logistic regression does not require grouping of observations to obtain valid estimates of effects and of outcome probabilities, and it has been shown in the binary case to provide more accurate probability e...

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 ...

logistic regression models are frequently used in clinicalresearch and particularly for modeling disease status and patientsurvival. in practice, clinical studies have several limitationsfor instance, in the study of rare diseases or due ethical considerations, we can only have small sample sizes. in addition, the lack of suitable andadvanced measuring instruments lead to non-precise observatio...