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High-dimensional variable selection

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We live in the era of Big Data, where it is often relatively cheap to collect and store vast quantities of data. While this presents unprecendented opportunities to improve our scientific understanding and quality of life, there are also substantial statistical challenges. One of the most important of these is variable selection, and it is a topic that has received enormous attention among statisticians over the last 15 years or so.

I will describe some methods that have been proposed, and outline some of my own work, in collaboration with Rajen Shah, that aims to improve the performance of any existing variable selection algorithm through Complementary Pairs Stability Selection.

This talk is part of the Cambridge Statistics Discussion Group (CSDG) series.

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