CORExpress v0.1.1

CORExpress® develops improved regression and classification models for: linear regression, logistic regression, linear discriminant analysis and survival models (Cox regression). CORExpress handles multicolinearity due to correlated predictors effectively even with high dimensional data (more variables than cases).

What Features of Regression Models are Improved?

CORExpress improves:

• interpretation of regression coefficients
• out-of-sample prediction
• classification
• variable selection

How does CORExpress Work?

• CORExpress (patent pending) develops regression models using Correlated Component Regression (CCR) methods. CCR was developed by Dr. Jay Magidson for simultaneously estimating regression models and selecting predictors from a potentially large number of candidate predictors. Reliable models are obtained using a fast algorithm that incorporates M-fold cross-validation to optimize tuning parameters (amount of regularization K and # predictor variables P).
• Final models may even include more predictors than cases!!! (impossible with traditional regression methods)

Can CORExpress Improve Latent Class Regression Models?
Yes. Latent GOLD® can be used to obtain segments, and CORExpress can be used to predict segment membership or to develop separate regression models for each segment. CORExpress allows many more predictor variables to be included in the model than possible previously.

General Overview
Regression modeling is undergoing a revolution precipitated by the availability of hundreds and even thousands of candidate predictor variables in genomics, but increasingly vast amounts of data are becoming available in all other fields as well. Problems in traditional regression modeling occur when the number of predictors P included in a model approaches or exceeds the sample size N. In this type of situation, involving the presence of ‘high-dimensional data’, traditional regression methods become unreliable and regression coefficients may even be impossible to estimate. Recent advances with high-dimensional data show how such problems can be resolved (see: Cai and Shen (2011)). This important new field continues to evolve at a rapid pace.

Statistical Innovations is pleased to announce our first new software innovation since Latent GOLD in 2000! CORExpress focuses on regression analysis (linear regression, logistic regression, etc.) where large numbers of correlated predictors may be available. On many data sets, it has been shown to outperform penalized regression techniques such as Lasso, and other methods such as Naive Bayes and PLS regression.

Homepage: http://statisticalinnovations.com/products/corexpress.html