Selection of a subset of meaningful covariates for a statistical model is an important and often time-consuming task in model building. Lawless and Singhal (1978, Biometrics 34, 318-327) proposed a ...

Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies.

To avoid these, penalized maximum likelihood estimates are introduced, which give estimates of the logistic parameters and a nonparametric spline estimate of the marginal distribution of x. Extensions ...

Regression can be used on categorical responses to estimate probabilities and to classify.

This article presents a complete demo program for logistic regression, using batch stochastic gradient descent training with weight decay. Compared to other binary classification techniques, logistic ...

The LOGISTIC and PROBIT procedures can perform logistic and ordinal logistic regression. See Chapter 5, "Introduction to Categorical Data Analysis Procedures," Chapter 39, "The LOGISTIC Procedure," ...