Borkar, Vivek S. ; Dwivedi, Raaz ; Sahasrabudhe, Neeraja (2016) Gaussian approximations in high dimensional estimation Systems & Control Letters, 92 . pp. 42-45. ISSN 0167-6911
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Official URL: http://doi.org/10.1016/j.sysconle.2016.03.001
Related URL: http://dx.doi.org/10.1016/j.sysconle.2016.03.001
Abstract
Several estimation techniques assume validity of Gaussian approximations for estimation purposes. Interestingly, these ensemble methods have proven to work very well for high-dimensional data even when the distributions involved are not necessarily Gaussian. We attempt to bridge the gap between this oft-used computational assumption and the theoretical understanding of why this works, by employing some recent results on random projections on low dimensional subspaces and concentration inequalities.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Elsevier Science. |
| ID Code: | 135189 |
| Deposited On: | 20 Jan 2023 05:43 |
| Last Modified: | 20 Jan 2023 05:43 |
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