Adaptation in log-concave density estimation
57 mins 56 secs,
830.35 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
1.91 Mbits/sec
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About this item
| Description: |
Kim, A
Tuesday 13th February 2018 - 11:00 to 12:00 |
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| Created: | 2018-02-13 13:50 |
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| Collection: | Statistical scalability |
| Publisher: | Isaac Newton Institute |
| Copyright: | Kim, A |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | The log-concave maximum likelihood estimator of a density on R^d on a sample of size n is known to attain the minimax optimal rate of convergence up to a log factor when d = 2 and d = 3. In this talk, I will review the univariate adaptation result, and will present new results on adaptation properties in the multivariate setting. This is based on joint work with Oliver Feng, Aditya Guntuboyina and Richard Samworth
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| WebM | 640x360 | 679.23 kbits/sec | 288.30 MB | View | Download | |
| iPod Video | 480x270 | 490.71 kbits/sec | 208.22 MB | View | Download | |
| MP3 | 44100 Hz | 249.73 kbits/sec | 106.09 MB | Listen | Download | |
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