Investopedia

Stepwise Regression Explained: Uses, Benefits, and Drawbacks

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
JSTOR Daily

A STEPWISE REGRESSION METHOD AND CONSISTENT MODEL SELECTION FOR HIGH-DIMENSIONAL SPARSE LINEAR MODELS

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...
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 ...
Nature

Variable selection in logistic regression for detecting SNP–SNP interactions: the rheumatoid arthritis example

Many complex disease traits are observed to be associated with single nucleotide polymorphism (SNP) interactions. In testing small-scale SNP–SNP interactions, variable selection procedures in logistic ...
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 ...
Computer Weekly

Oxagile on predictive analytics: 3 types of regression algorithms you should know

The latest trends in software development from the Computer Weekly Application Developer Network. This is a guest post for the Computer Weekly Developer Network written by Yana Yelina in her role as ...