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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 models equally compatible with the data. I will outline a different approach whose aim is essentially a confidence set of effective simple representations. The method hinges on the ability to make initially a very large number of separate analyses, allowing each explanatory feature to be assessed in combination with many other such features. A probabilistic assessment of the method will be given.
The talk is based on joint work with David R Cox.
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