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Activity Number: 88
Type: Invited
Date/Time: Monday, August 7, 2006 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #305279
Title: Logistic Regression on Autopilot
Author(s): Trevor Hastie*+
Companies: Stanford University
Address: Statistics Department, Stanford, CA, 94305,
Keywords: boosting ; LASSO
Abstract:

Logistic regression is a standard modeling tool in biopharmaceutical applications. It typically is used in controlled settings with a limited number of variables and low-order interactions, if present. In this talk, I will discuss two procedures for automating logistic regression modeling with large datasets: GLMpath fits an entire lasso sequence of logistic regression models in an efficient manner and tree-based gradient boosting does the same with automatic detection of interaction effects.

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