|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Local Bilinear Multiple-Output Quantile Regression: from $L_1$ Optimization to Regression Depth
If you have a question about this talk, please contact Richard Samworth.
A new multiple output concept of quantile regression, based on a directional version of Koenker and Bassett?s traditional one, has been introduced in Hallin, Paindaveine and Siman (Annals of Statistics 2010, 635-703), essentially for multivariate location problems. The empirical counterpart of that concept produces polyhedral contours that (in the location case) coincide with the Tukey halfspace depth contours. In a regression context, however, that concept cannot account for nonlinear or/and heteroscedastic dependencies. A local bilinear version of those contours is proposed here, which asymptotically recovers the conditional halfspace depth contours of the multiple-output response. A Bahadur representation is established, along with asymptotic normality results. Examples are provided.
This talk is part of the Statistics series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsHistory of Medicine Seminars Group Theory, Geometry and Representation Theory: Abel Prize 2008 Organic Chemistry
Other talksAbsolute measures of effectiveness The Festival of Ideas @ POLIS: Future Worlds, future security threats Respiratory Health and Smoking Science Summit 2016 Epigenetic inheritance and parent-of-origin effects Learning from Students to Shape Plant Sciences Teaching for the 21st Century Platelets in time and space: understanding the actions of anti-platelet drugs within the body