Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

This paper describes a method for a model-based analysis of clinical safety data called multivariate Bayesian logistic regression (MBLR). Parallel logistic regression models are fit to a set of ...

The relationship between normal variable discrimination analysis and logistic regression analysis is noted and tests for the equality of two or more sets of multivariate normal parameters based on a ...

Purpose To calculate and validate a linear discriminant function (LDF) for scanning laser polarimetry (SLP) with variable corneal compensation (GDx-VCC) to increase the diagnostic accuracy when using ...

"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count data. The most ...

Increased triglyceride-glucose (TyG) index values are strongly associated with decreased lung function in healthy individuals.