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Large numbers of explanatory variables

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STS - Statistical scalability

The lasso and its variants are powerful methods for regression analysis when there are a small number of study individuals and a large number of potential explanatory variables. There results a single model, while there may be several simple representations equally compatible with the data. I will outline a different approach, whose aim is essentially a confidence set of models. A probabilistic assessment of the method will be given. 

The talk is based on joint work with David R Cox.

This talk is part of the Isaac Newton Institute Seminar Series series.

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