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Robert Tibshirani - The Lasso and interaction models

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Vortrag Robert Tibshirani (USA) über "The Lasso and interaction models"

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  • FDM-Seminar
Wann 11.07.2012
von 11:15 bis 12:15
Wo Hörsaal Virologie, Hermann-Herder-Straße 11
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This is part of Robert Tibshirani and Mini-Symposium

Special date: Wednesday!

Raumänderung: Hörsaal Virologie, Hermann-Herder-Straße 11

 

Robert Tibshirani Professor of Statistics and Health Research and Policy

Stanford University School of Medicine, California (USA)

The Lasso is a popular tool for high-dimensional model building. First I will review recent computational advances that enable the Lasso to be applied to large datasets. Then I will describe very recent work on fitting interaction models. Statisticians commonly demand that an interaction only be included in a model if both variables are marginally important. We study the problem of identifying hierarchical two-way interaction models from the viewpoint of the Lasso (i.e., L1-penalized regression). We show that by adding a set of convex constraints to the Lasso problem, we can produce sparse interaction models that honor the hierarchy restriction. In contrast to stepwise procedures that are most commonly used for building interaction models, our formulation is convex, and its solution is completely characterized by a set of optimality conditions. This makes it easier to study as a statistical estimator. We argue that restricting to hierarchical interactions can be advantageous both statistically and computationally. We study its properties, give examples and present an efficient computational algorithm.

This is based on the PhD thesis work of my student Jacob Bien and is also joint with Jonathan Taylor.

 

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