COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Probability Theory and Statistics in High and Infinite Dimensions > Adaptation in some shape-constrained regression problems

## Adaptation in some shape-constrained regression problemsAdd to your list(s) Download to your calendar using vCal - Adityanand Guntuboyina, University of California, Berkeley,
- Monday 23 June 2014, 16:45-17:15
- Centre for Mathematical Sciences, Meeting Room 2.
If you have a question about this talk, please contact clc32. We consider the problem of estimating a normal mean constrained to be in a convex polyhedral cone in Euclidean space. We say that the true mean is sparse if it belongs to a low dimensional face of the cone. We show that, in a certain natural subclass of these problems, the maximum likelihood estimator automatically adapts to sparsity in the underlying true mean. We discuss the problems of convex regression and univariate and bivariate isotonic regression as examples. This talk is part of the Probability Theory and Statistics in High and Infinite Dimensions series. ## This talk is included in these lists:- All CMS events
- All Talks (aka the CURE list)
- CMS Events
- Centre for Mathematical Sciences, Meeting Room 2
- DPMMS Lists
- DPMMS info aggregator
- DPMMS lists
- School of Physical Sciences
- Statistical Laboratory info aggregator
Note that ex-directory lists are not shown. |
## Other listsInteresting talks- 1st try "Existential Risk" screening and Q & A Computer Laboratory talks## Other talksVisual hallucinations in Parkinson’s disease - imbalances in top-down vs. bottom up information processing Rather more than Thirty-Nine Steps: the life of John Buchan To be confirmed Designing Active Macroscopic Heat Engines Seminar – Why do policymakers seem to ignore your evidence? Epigenetics: One Genome, Multiple Phenotypes |