JSTOR Daily

Controlling for overdispersion in grouped conditional logit models: A computationally simple application of Dirichlet-multinomial regression

In this article, we provide a random utility-based derivation of the Dirichlet-multinomial regression and propose it as a convenient alternative for dealing with overdispersed multinomial data. We ...
technologynetworks

Logistic Regression

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 ...
ascopubs.org

Development of a Machine Learning Model to Identify Colorectal Cancer Stage in Medicare Claims

Automated Classification of Breast Cancer Across the Spectrum of ERBB2 Expression Focusing on Heterogeneous Tumors With Low Human Epidermal Growth Factor Receptor 2 Expression We included patients age ...
JSTOR Daily

Bias correction for the proportional odds logistic regression model with application to a study of surgical complications

The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is ...
Visual Studio Magazine

Logistic Regression Using PyTorch with L-BFGS

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...