Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

The LOGISTIC, GENMOD, PROBIT, and CATMOD procedures can all be used for statistical modeling of categorical data. The CATMOD procedure provides maximum likelihood estimation for logistic regression, ...

This is a preview. Log in through your library . Abstract This paper rigorously establishes that the existence of the maximum likelihood estimate (MLE) in high-dimensional logistic regression models ...

X ij = [x ij1, ... , x ijp]' The Generalized Estimating Equation of Liang and Zeger (1986) for estimating the p ×1 vector of regression parameters is an extension of the independence estimating ...

Matrix-covariate is now frequently encountered in many biomedical researches. It is common to fit conventional statistical models by vectorizing matrix-covariate. This strategy results in a large ...

A machine learning model using basic clinical data can predict PH risk, identifying key predictors like low hemoglobin and elevated NT-proBNP. Researchers have developed a machine learning model that ...