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A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_19C | Tue, 8/5/2014, 8:00 AM - 12:00 PM | CC-159 | |
| Regression Modeling with Many Correlated Predictors: Big Data in Practice — Professional Development Continuing Education Course | |||
| ASA | |||
| The availability of a vast amount of data, in fields such as genomics, marketing research and signal processing, has led to recent advances in high-dimensional data analysis. It is now possible to develop reliable regression models 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. The key to developing reliable predictions is to use regularized regression methods. We introduce and compare the 2 primary regularization approaches for analyzing such data - penalized regression and component methods, related software, and also introduce recent advances in feature selection. Our applications-oriented presentation provides insight into how the new approaches work in examples with both low and high-dimensional data, 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 methods. The material presented will be included in a forthcoming book on this topic by Jay Magidson. | |||
| Instructor(s): Tony Babinec, AB Analytics, Jay Magidson, Statistical Innovations | |||
2014 JSM Online Program Home
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