Name
Title | ||
|---|---|---|
Robust bootstrap methods with an application to geolocation in harsh LOS/NLOS environments |
Abstract | ||
|---|---|---|
The bootstrap is a powerful computational tool for statistical inference that allows for the estimation of the distribution of an estimate without distributional assumptions on the underlying data, reliance on asymptotic results or theoretical derivations. On the other hand, robustness properties of the bootstrap in the presence of outliers are very poor, irrespective of the robustness of the underlying estimator. This motivates the need to robustify the bootstrap procedure itself. Improvements to two existing robust bootstrap methods are suggested and a novel approach for robustifying the bootstrap is introduced. The methods are compared in a simulation study and the proposed method is applied to robust geolocation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6855156 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
estimation theory,mobility management (mobile radio),statistical distributions,distribution estimation,geolocation application,harsh line-of-sight environments,nonline-of-sight environments,robust bootstrap method,robust geolocation,statistical inference,bootstrap,geolocation,regression,robust | Non-line-of-sight propagation,Data mining,Computer science,Geolocation,Outlier,Robustness (computer science),Statistical inference,Bootstrapping (electronics),Estimator | Conference |
ISSN | Citations | PageRank |
1520-6149 | 2 | 0.54 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Vlaski | 1 | 23 | 11.39 |
| Michael Muma | 2 | 144 | 19.51 |
| Abdelhak M. Zoubir | 3 | 1036 | 148.03 |