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.
Date and Time
Wednesday, November 16, 2016 at 2:00 pm EST (GMT -4)
(In a different time zone?)
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.
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You’ll get exclusive access to this month’s webinar on LASSO models, plus live, open Q&A sessions, video recordings of all the monthly content, and more.