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Unbiased Estimators and Multilevel Monte Carlo
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Zeitschriftentitel: | Operations Research |
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Personen und Körperschaften: | |
In: | Operations Research, 66, 2018, 2, S. 448-462 |
Medientyp: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Institute for Operations Research and the Management Sciences (INFORMS)
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Schlagwörter: |
author_facet |
Vihola, Matti Vihola, Matti |
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author |
Vihola, Matti |
spellingShingle |
Vihola, Matti Operations Research Unbiased Estimators and Multilevel Monte Carlo Management Science and Operations Research Computer Science Applications |
author_sort |
vihola, matti |
spelling |
Vihola, Matti 0030-364X 1526-5463 Institute for Operations Research and the Management Sciences (INFORMS) Management Science and Operations Research Computer Science Applications http://dx.doi.org/10.1287/opre.2017.1670 <jats:p>Multilevel Monte Carlo (MLMC) and recently proposed unbiased estimators are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction provided by the new schemes can be substantial.</jats:p> Unbiased Estimators and Multilevel Monte Carlo Operations Research |
doi_str_mv |
10.1287/opre.2017.1670 |
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Institute for Operations Research and the Management Sciences (INFORMS), 2018 |
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Institute for Operations Research and the Management Sciences (INFORMS), 2018 |
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2018 |
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Institute for Operations Research and the Management Sciences (INFORMS) |
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Operations Research |
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49 |
title |
Unbiased Estimators and Multilevel Monte Carlo |
title_unstemmed |
Unbiased Estimators and Multilevel Monte Carlo |
title_full |
Unbiased Estimators and Multilevel Monte Carlo |
title_fullStr |
Unbiased Estimators and Multilevel Monte Carlo |
title_full_unstemmed |
Unbiased Estimators and Multilevel Monte Carlo |
title_short |
Unbiased Estimators and Multilevel Monte Carlo |
title_sort |
unbiased estimators and multilevel monte carlo |
topic |
Management Science and Operations Research Computer Science Applications |
url |
http://dx.doi.org/10.1287/opre.2017.1670 |
publishDate |
2018 |
physical |
448-462 |
description |
<jats:p>Multilevel Monte Carlo (MLMC) and recently proposed unbiased estimators are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction provided by the new schemes can be substantial.</jats:p> |
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author | Vihola, Matti |
author_facet | Vihola, Matti, Vihola, Matti |
author_sort | vihola, matti |
container_issue | 2 |
container_start_page | 448 |
container_title | Operations Research |
container_volume | 66 |
description | <jats:p>Multilevel Monte Carlo (MLMC) and recently proposed unbiased estimators are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction provided by the new schemes can be substantial.</jats:p> |
doi_str_mv | 10.1287/opre.2017.1670 |
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imprint | Institute for Operations Research and the Management Sciences (INFORMS), 2018 |
imprint_str_mv | Institute for Operations Research and the Management Sciences (INFORMS), 2018 |
institution | DE-D275, DE-Bn3, DE-Brt1, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
issn | 0030-364X, 1526-5463 |
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language | English |
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mega_collection | Institute for Operations Research and the Management Sciences (INFORMS) (CrossRef) |
physical | 448-462 |
publishDate | 2018 |
publishDateSort | 2018 |
publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
record_format | ai |
recordtype | ai |
series | Operations Research |
source_id | 49 |
spelling | Vihola, Matti 0030-364X 1526-5463 Institute for Operations Research and the Management Sciences (INFORMS) Management Science and Operations Research Computer Science Applications http://dx.doi.org/10.1287/opre.2017.1670 <jats:p>Multilevel Monte Carlo (MLMC) and recently proposed unbiased estimators are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction provided by the new schemes can be substantial.</jats:p> Unbiased Estimators and Multilevel Monte Carlo Operations Research |
spellingShingle | Vihola, Matti, Operations Research, Unbiased Estimators and Multilevel Monte Carlo, Management Science and Operations Research, Computer Science Applications |
title | Unbiased Estimators and Multilevel Monte Carlo |
title_full | Unbiased Estimators and Multilevel Monte Carlo |
title_fullStr | Unbiased Estimators and Multilevel Monte Carlo |
title_full_unstemmed | Unbiased Estimators and Multilevel Monte Carlo |
title_short | Unbiased Estimators and Multilevel Monte Carlo |
title_sort | unbiased estimators and multilevel monte carlo |
title_unstemmed | Unbiased Estimators and Multilevel Monte Carlo |
topic | Management Science and Operations Research, Computer Science Applications |
url | http://dx.doi.org/10.1287/opre.2017.1670 |