Member Training: The LASSO Regression Model

The LASSO model (Least Absolute Shrinkage and Selection Operator) is a recent development that allows you to find a good fitting model in the regression context. It avoids many of the problems of overfitting that plague other model-building approaches.

In this Statistically Speaking Training, guest instructor Steve Simon, PhD, explains what overfitting is — and why it’s a problem.

Then he illustrates the geometry of the LASSO model in comparison to other regression approaches, ridge regression and stepwise variable selection.

Finally, he shows you how LASSO regression works with a real data set.

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About the Instructor

Steve Simon works as an independent statistical consultant and as a part-time faculty member in the Department of Biomedical and Health Informatics at the University of Missouri-Kansas City. He has previously worked at Children’s Mercy Hospital, the National Institute for Occupational Safety and Health, and Bowling Green State University.

Steve has over 90 peer-reviewed publications, four of which have won major awards. He has written one book, Statistical Evidence in Medical Trials, and is the author of a major website about Statistics, Research Design, and Evidence Based Medicine, www.pmean.com. One of his current areas of interest is using Bayesian models to forecast patient accrual in clinical trials. Steve received a Ph.D. in Statistics from the University of Iowa in 1982.

It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.