| dc.creator | Lockhart, RA | |
| dc.creator | Samworth, Richard John | |
| dc.date.accessioned | 2017-05-08 | |
| dc.date.accessioned | 2018-11-24T23:27:31Z | |
| dc.date.available | 2017-09-26T14:46:23Z | |
| dc.date.available | 2018-11-24T23:27:31Z | |
| dc.date.issued | 2017-12-01 | |
| dc.identifier | https://www.repository.cam.ac.uk/handle/1810/267417 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/123456789/3997 | |
| dc.description.abstract | We congratulate the authors on their stimulating contribution to the burgeoning high-dimensional inference literature. The bootstrap offers such an attractive methodology in these settings, but it is well-known that its naive application in the context of shrinkage/superefficiency is fraught with danger (e.g. Samworth, 2003; Chatterjee and Lahiri, 2011). The authors show how these perils can be elegantly sidestepped by working with de-biased, or de-sparsified, versions of estimators. | |
| dc.language | en | |
| dc.publisher | Sociedad de Estadistica e Investigacion Operativa | |
| dc.publisher | Test | |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | |
| dc.rights | Attribution 4.0 International | |
| dc.rights | Attribution 4.0 International | |
| dc.rights | Attribution 4.0 International | |
| dc.rights | Attribution 4.0 International | |
| dc.subject | Confidence intervals | |
| dc.subject | De-biased estimator | |
| dc.subject | High-dimensional inference | |
| dc.title | Comments on: High-dimensional simultaneous inference with the bootstrap | |
| dc.type | Article | |
