University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Large numbers of explanatory variables

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Large numbers of explanatory variables

Download to your calendar using vCal

If you have a question about this talk, please contact INI IT .

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.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

MODO Gallery, 62 Sidney Street, Cambridge Room SG1/2, Alison Richard Building, Sidgwick Site, 7 West Road, Cambridge, CB3 9DT Recital Room, Churchill College, Storey’s Way, Cambridge. CB3 0DS

Other talks

An Amphibious Urbanism | | Cut 4: The Desert Arrives Slowly Title TBC Morning Break The uplift and erosion history of the Gamburtsev Subglacial Mountains and their role in the formation of the East Antarctic Ice Sheet The Inaccessible Game The rise of local cartography in Europe, 12th-14th century
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity