JSM 2011 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Abstract Details

Activity Number: 448
Type: Invited
Date/Time: Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and Marketing
Abstract - #300008
Title: Conditional Regression Models
Author(s): William D. Heavlin*+
Companies: Google Inc.
Address: 1600 Amphitheatre Pkwy, Mountain View, CA, 94043 ,
Keywords: boosting ; causal inference ; likelihood ; machine learning ; noise factors ; von Mises-Fisher distribution

Because of the large volumes of data available, many web traffic investigations progress successfully using only simple two-sample experiments. However, an important subset of studies -- those dealing with rare events, causal inference, and/or measurement validation -- benefit from fine-grained blocking. By forming likelihoods free of the block nuisance parameters, conditional models point to a mathematically compact, parsimonious approach. Further, the conditional Gaussian, conditional Poisson, and conditional logistic models all share a single functional form. This talk discusses why one might want apply conditional models, how to estimate their coefficients and compute them efficiently, and how to adapt and deploy them as predictive models. We illustrate with a variety of Google examples.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2011 program

2011 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.