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Article: Minimum variance unbiased estimation based on bootstrap iterations
Title | Minimum variance unbiased estimation based on bootstrap iterations |
---|---|
Authors | |
Keywords | Bias Bootstrap Iteration Mle Monte Carlo Mvue |
Issue Date | 2006 |
Publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174 |
Citation | Statistics And Computing, 2006, v. 16 n. 3, p. 267-277 How to Cite? |
Abstract | Practical computation of the minimum variance unbiased estimator (MVUE) is often a difficult, if not impossible, task, even though general theory assures its existence under regularity conditions. We propose a new approach based on iterative bootstrap bias correction of the maximum likelihood estimator to accurately approximate the MVUE. Viewing bootstrap iteration as a Markov process, we develop a computational algorithm for bias correction based on arbitrarily many bootstrap iterations. The algorithm, when applied parametrically to finite sample spaces, does not involve Monte Carlo simulation. For infinite sample spaces, a nonparametric version of the algorithm is combined with a preliminary round of Monte Carlo simulation to yield an approximate MVUE. Both algorithms are computationally more efficient and stable than conventional simulation-based bootstrap iterations. Examples are given of both finite and infinite sample spaces to illustrate the effectiveness of our new approach. © Springer Science + Business Media, LLC 2006. |
Persistent Identifier | http://hdl.handle.net/10722/172422 |
ISSN | 2023 Impact Factor: 1.6 2023 SCImago Journal Rankings: 0.923 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chan, KYF | en_US |
dc.contributor.author | Lee, SMS | en_US |
dc.contributor.author | Ng, KW | en_US |
dc.date.accessioned | 2012-10-30T06:22:25Z | - |
dc.date.available | 2012-10-30T06:22:25Z | - |
dc.date.issued | 2006 | en_US |
dc.identifier.citation | Statistics And Computing, 2006, v. 16 n. 3, p. 267-277 | en_US |
dc.identifier.issn | 0960-3174 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/172422 | - |
dc.description.abstract | Practical computation of the minimum variance unbiased estimator (MVUE) is often a difficult, if not impossible, task, even though general theory assures its existence under regularity conditions. We propose a new approach based on iterative bootstrap bias correction of the maximum likelihood estimator to accurately approximate the MVUE. Viewing bootstrap iteration as a Markov process, we develop a computational algorithm for bias correction based on arbitrarily many bootstrap iterations. The algorithm, when applied parametrically to finite sample spaces, does not involve Monte Carlo simulation. For infinite sample spaces, a nonparametric version of the algorithm is combined with a preliminary round of Monte Carlo simulation to yield an approximate MVUE. Both algorithms are computationally more efficient and stable than conventional simulation-based bootstrap iterations. Examples are given of both finite and infinite sample spaces to illustrate the effectiveness of our new approach. © Springer Science + Business Media, LLC 2006. | en_US |
dc.language | eng | en_US |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0960-3174 | en_US |
dc.relation.ispartof | Statistics and Computing | en_US |
dc.subject | Bias | en_US |
dc.subject | Bootstrap Iteration | en_US |
dc.subject | Mle | en_US |
dc.subject | Monte Carlo | en_US |
dc.subject | Mvue | en_US |
dc.title | Minimum variance unbiased estimation based on bootstrap iterations | en_US |
dc.type | Article | en_US |
dc.identifier.email | Lee, SMS: smslee@hku.hk | en_US |
dc.identifier.email | Ng, KW: kaing@hkucc.hku.hk | en_US |
dc.identifier.authority | Lee, SMS=rp00726 | en_US |
dc.identifier.authority | Ng, KW=rp00765 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1007/s11222-006-8078-8 | en_US |
dc.identifier.scopus | eid_2-s2.0-33745597331 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-33745597331&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 16 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.spage | 267 | en_US |
dc.identifier.epage | 277 | en_US |
dc.identifier.isi | WOS:000238746900005 | - |
dc.publisher.place | United States | en_US |
dc.identifier.scopusauthorid | Chan, KYF=7406035182 | en_US |
dc.identifier.scopusauthorid | Lee, SMS=24280225500 | en_US |
dc.identifier.scopusauthorid | Ng, KW=7403178774 | en_US |
dc.identifier.citeulike | 824395 | - |
dc.identifier.issnl | 0960-3174 | - |