|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 listsInstitution of Civil Engineers (Cambridge Branch) Graduate Women's Network International
Other talksCognitive development assessed by object manipulation in great apes and humans Big Dream Documentary Screening and Q&A Panel Building Stones of Cambridge. Geology walking tour. Causes and effects of recent changes in Antarctic sea-ice transport The Relative Merits of Alternative Goals of Development: Happiness, Income or Human Glocalizing Medicine in the Canton/Hong Kong Region in Late Qing China (1840-1911)