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The workshop is on the following topic: Prediction problems when the number of features(variables) is much larger than the number of observations. Such problems have become of increasing importance in areas such as genomics, computational biology, and chemometrics. The analysis of such data requires either modification of the usual approaches designed for sample size larger than number of variables, or entirely new procedures.
Workshop
Regression Modeling with Many Correlated Predictors
Jay Magidson, Statistical Innovations
Tony Babinec, AB Analytics
Friday, April 8, 2011
8:30 AM – 4:30 PM
Rush University Medical Center
1653 W Congress Parkway, Chicago, IL 60612
Sponsored by the Chicago Chapter of the American Statistical Association
Abstract:
Recent advances in analysis of high dimensional data now allow reliable regression models to be developed even when the number of predictors exceeds the number of cases! In this course we begin by reviewing problems and limitations with traditional linear and logistic regression. Our applications-oriented presentation provides insight into how the new approaches work through examples and by providing an overview of the relevant theory, supplemented by the supporting equations. We use real and simulated data sets to illustrate the different approaches.
For more information or to register, please visit the Chicago Chapter website:
http://www.chicagoasa.org/.
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