| dc.creator | Maronna, Ricardo Antonio | |
| dc.creator | Yohai, Victor Jaime | |
| dc.date | 2015-03 | |
| dc.date | 2020-08-07T15:21:10Z | |
| dc.date.accessioned | 2023-07-14T20:36:57Z | |
| dc.date.available | 2023-07-14T20:36:57Z | |
| dc.identifier | http://sedici.unlp.edu.ar/handle/10915/101650 | |
| dc.identifier | https://ri.conicet.gov.ar/11336/42723 | |
| dc.identifier | issn:0167-9473 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7439931 | |
| dc.description | Good robust estimators can be tuned to combine a high breakdown point and a specified asymptotic efficiency at a central model. This happens in regression with MM- and -estimators among others. However, the finite-sample efficiency of these estimators can be much lower than the asymptotic one. To overcome this drawback, an approach is proposed for parametric models, which is based on a distance between parameters. Given a robust estimator, the proposed one is obtained by maximizing the likelihood under the constraint that the distance is less than a given threshold. For the linear model with normal errors, simulations show that the proposed estimator attains a finite-sample efficiency close to one while improving the robustness of the initial estimator. The same approach also shows good results in the estimation of multivariate location and scatter. | |
| dc.description | Facultad de Ciencias Exactas | |
| dc.format | application/pdf | |
| dc.format | 262-274 | |
| dc.language | en | |
| dc.rights | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | |
| dc.subject | Matemática | |
| dc.subject | Linear model | |
| dc.subject | Robust estimator | |
| dc.subject | High efficiency | |
| dc.title | High finite-sample efficiency and robustness based on distance-constrained maximum likelihood | |
| dc.type | Articulo | |
| dc.type | Preprint | |
