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 month’s Statistically Speaking webinar, guest instructor Steve Simon, PhD, will explain what overfitting is — and why it’s a problem.
Then he’ll illustrate the geometry of the LASSO model in comparison to other regression approaches, ridge regression and stepwise variable selection.
Finally, he’ll show you how LASSO regression works with a real data set.
Note: This webinar is an exclusive benefit for members of the Statistically Speaking Membership Program.
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.
Not a Member Yet?
It’s never too late to join the hottest stats club around.
Just head over to our enrollment page to sign up for Statistically Speaking.
You’ll get exclusive access to this month’s webinar live, weekly live Q&A sessions, a private stats forum, 60+ recordings of past webinars (including this one), and more.
