Leverage Sampling for Single-Index Models
Metadata Field | Value | Language |
---|---|---|
dc.contributor.advisor | Abebe, Asheber | |
dc.contributor.author | Almutairi, Basmah | |
dc.date.accessioned | 2021-01-04T16:55:41Z | |
dc.date.available | 2021-01-04T16:55:41Z | |
dc.date.issued | 2021-01-04 | |
dc.identifier.uri | https://etd.auburn.edu//handle/10415/7552 | |
dc.description.abstract | In this thesis, a generalized leverage-based sub-sampling method for single-index models is proposed. The approach gives more efficient estimators than random sub-samples of the same size. Also, robust rank-based estimators of single-index models using leverage sub-samples provide estimators that are robust to outliers and heavy tails. A common bottleneck for rank-based estimators is the lack of computational efficiency, which is overcome using sub- samples. A simulation study was performed and, as expected the rank-based index direction estimator was comparable to the least squares index direction estimator when the errors follow a normal distribution. However, the rank-based index direction estimator was more efficient when the data followed a heavy-tailed error distribution. Finally, the results from a real data example are presented to highlight the performance of the proposed estimators. | en_US |
dc.rights | EMBARGO_GLOBAL | en_US |
dc.subject | Mathematics and Statistics | en_US |
dc.title | Leverage Sampling for Single-Index Models | en_US |
dc.type | Master's Thesis | en_US |
dc.embargo.length | MONTHS_WITHHELD:60 | en_US |
dc.embargo.status | EMBARGOED | en_US |
dc.embargo.enddate | 2026-01-06 | en_US |