Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications.

Linear regression is a statistical method used to understand the relationship between an outcome variable and one or more explanatory variables. It works by fitting a regression line through the ...

This study examines how trade shocks that start in one large economy ripple through other countries and how long those ...

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 ...

Tadikamalla and Johnson [Biometrika 69 (1982) 461–465] developed the LB distribution to variables with bounded support by considering a transformation of the standard Logistic distribution. In this ...

Using a Bayesian logistic regression model, they examined data from 1,273 epilepsy cases, of which 287 were SUDEP and 986 controls, and 22 clinical predictor variables.

Typical examples of model-based designs include the continual reassessment method (CRM 5) and its variations, such as the escalation with overdose control, 6 the Bayesian logistic regression model, 7 ...