Block sizes are ... investigated using ordinal logistic regression with treatment group included as the main effect. Secondary outcomes measured on an interval scale will be assessed using linear ...

We set the threshold to 0.9 and retain the features with a correlation coefficient less than 0.9 as the input features of the LASSO (Least Absolute Shrinkage and Selection Operator) regression model .

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 ...

In this paper, we proposed a framework, called Mulr4FL, for fault localization using a multivariate logistic regression model that combined both static and dynamic features collected from the program ...

The aim of the course is to make the participants familiar with advanced statistical regression methods applied to clinical research and epidemiology. This will give the participants a better basis ...

Abstract: The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multiplications ...