Leverage Sampling for Single-Index Models
| dc.contributor.advisor | Asheber Abebe | |
| dc.contributor.author | Basmah Muawwadh Almutairi | |
| dc.date | 2020 | |
| dc.date.accessioned | 2022-06-01T20:32:22Z | |
| dc.date.available | 2022-06-01T20:32:22Z | |
| dc.degree.department | Statistics | |
| dc.degree.grantor | College of Science and Mathematics | |
| 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. | |
| dc.identifier.uri | https://drepo.sdl.edu.sa/handle/20.500.14154/58532 | |
| dc.language.iso | en | |
| dc.title | Leverage Sampling for Single-Index Models | |
| sdl.thesis.level | Master | |
| sdl.thesis.source | SACM - United States of America |