Deconfounding using Spectral Transformations
33 mins 44 secs,
491.32 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
1.94 Mbits/sec
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About this item
| Description: |
Bühlmann, P
Ćevid, D Friday 29th June 2018 - 11:45 to 12:30 |
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| Created: | 2018-06-29 16:00 |
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| Collection: | Statistical scalability |
| Publisher: | Isaac Newton Institute |
| Copyright: | Bühlmann, P Ćevid, D |
| 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: | High-dimensional regression methods which rely on the sparsity of the ground truth, such as the Lasso, might break down in the presence of confounding variables. If a latent variable affects both the response and the predictors, the correlation between them changes. This phenomenon can be represented as a linear model where the sparse coefficient vector has been perturbed. We will present our work on this problem. We investigate and propose some spectral transformations for the data which serve as input for the Lasso. We discuss assumptions for achieving the optimal error rate and illustrate the performance on a genomic dataset. The approach is easy to use and leads to convincing results. The talk is based on joint work with Nicolai Meinshausen. |
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| MPEG-4 Video * | 640x360 | 1.94 Mbits/sec | 491.32 MB | View | Download | |
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