Legend: Palais des congrès de Montréal = CC, Le Westin Montréal = W, Intercontinental Montréal = I
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_35T | Wed, 8/7/2013, 1:00 PM - 2:45 PM | W-St. Antoine | |
| Introduction to Modern Regression Analysis Techniques: Linear, Logistic, Nonlinear, Regularized, GPS (Generalized Path Seeker), Lars, Lasso, Elastic Net, and Mars (Multivariate Adaptive Regression Splines) — Continuing Education CTW | |||
| ASA , Salford Systems | |||
| Instructor(s): Mikhail Golovnya, Salford Systems | |||
| Using real-world datasets we will demonstrate Stanford Professor Jerome Friedman's advances in nonlinear, regularized-linear and logistic regression. This workshop will introduce the main concepts behind Friedman's GPS and MARS, modern regression tools that can help analysts quickly develop superior predictive models. GPS includes classic techniques such as ridge and lasso regression, and also adds the new sub-lasso model, as well as intermediate modeling strategies. GPS gives ultra-fast modeling with massive numbers of predictors, powerful predictor selection and coefficient shrinkage. Clear tradeoff diagrams between model complexity and predictive accuracy allow modelers to select an ideal balance. Linear regression models, including GPS, fit straight lines to data. Although this usually oversimplifies the data structure, the approximation is often good enough for practical purposes. However, in the frequent situations in which a straight line is inappropriate, an expert modeler must search tediously for transformations to find the right curve. MARS is a nonlinear automated regression tool that automatically discovers complex patterns in the data. It automates the model specification search, including variable selection, variable transformation, interaction detection, missing value handling, and model validation. MARS approaches model construction more flexibly, allowing for bends, thresholds, and other departures from straight lines from the beginning. | |||
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