JSM 2011 Online Program

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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
Abstract:

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.


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