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Article: On m out of n bootstrapping for nonstandard M-estimation with nuisance parameters
Title | On m out of n bootstrapping for nonstandard M-estimation with nuisance parameters |
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Authors | |
Keywords | Gaussian process M out of n bootstrap M-estimator Nuisance parameter Subsampling |
Issue Date | 2006 |
Publisher | American Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main |
Citation | Journal Of The American Statistical Association, 2006, v. 101 n. 475, p. 1185-1197 How to Cite? |
Abstract | Nonstandard M-estimation, with nuisance parameters consistently estimated in the criterion function, often yields M-estimators converging weakly at rates different from n1/2 with weak limits that are typically non-Gaussian. The complicated asymptotics involved makes distributional estimation of the M-estimators analytically prohibitive. We show that the problem is resolved by m out of n bootstrapping under very general conditions, which provides a universal and convenient approach to consistently estimating sampling distributions of M-estimators. We illustrate our findings with applications to least median of squares regression estimators, studentized location M-estimators, shorth estimators, and robust M-estimators derived from L r-type loss functions. We provide empirical evidence using a simulation study to construct confidence intervals and globally estimate sampling distributions. © 2006 American Statistical Association. |
Persistent Identifier | http://hdl.handle.net/10722/82875 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 3.922 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Lee, SMS | en_HK |
dc.contributor.author | Pun, MC | en_HK |
dc.date.accessioned | 2010-09-06T08:34:24Z | - |
dc.date.available | 2010-09-06T08:34:24Z | - |
dc.date.issued | 2006 | en_HK |
dc.identifier.citation | Journal Of The American Statistical Association, 2006, v. 101 n. 475, p. 1185-1197 | en_HK |
dc.identifier.issn | 0162-1459 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/82875 | - |
dc.description.abstract | Nonstandard M-estimation, with nuisance parameters consistently estimated in the criterion function, often yields M-estimators converging weakly at rates different from n1/2 with weak limits that are typically non-Gaussian. The complicated asymptotics involved makes distributional estimation of the M-estimators analytically prohibitive. We show that the problem is resolved by m out of n bootstrapping under very general conditions, which provides a universal and convenient approach to consistently estimating sampling distributions of M-estimators. We illustrate our findings with applications to least median of squares regression estimators, studentized location M-estimators, shorth estimators, and robust M-estimators derived from L r-type loss functions. We provide empirical evidence using a simulation study to construct confidence intervals and globally estimate sampling distributions. © 2006 American Statistical Association. | en_HK |
dc.language | eng | en_HK |
dc.publisher | American Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main | en_HK |
dc.relation.ispartof | Journal of the American Statistical Association | en_HK |
dc.subject | Gaussian process | en_HK |
dc.subject | M out of n bootstrap | en_HK |
dc.subject | M-estimator | en_HK |
dc.subject | Nuisance parameter | en_HK |
dc.subject | Subsampling | en_HK |
dc.title | On m out of n bootstrapping for nonstandard M-estimation with nuisance parameters | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0162-1459&volume=101&spage=1185&epage=1197&date=2006&atitle=On+m+out+of+n+bootstrapping+for+nonstandard+M-estimation+with+nuisance+parameters | en_HK |
dc.identifier.email | Lee, SMS: smslee@hku.hk | en_HK |
dc.identifier.authority | Lee, SMS=rp00726 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1198/016214506000000014 | en_HK |
dc.identifier.scopus | eid_2-s2.0-33748882127 | en_HK |
dc.identifier.hkuros | 124254 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-33748882127&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 101 | en_HK |
dc.identifier.issue | 475 | en_HK |
dc.identifier.spage | 1185 | en_HK |
dc.identifier.epage | 1197 | en_HK |
dc.identifier.eissn | 1537-274X | - |
dc.identifier.isi | WOS:000240158700034 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Lee, SMS=24280225500 | en_HK |
dc.identifier.scopusauthorid | Pun, MC=14625683300 | en_HK |
dc.identifier.citeulike | 894885 | - |
dc.identifier.issnl | 0162-1459 | - |