A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters
In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2)...
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| Veröffentlicht in: | Operations research 2018-03, Vol.66 (2), p.487-499 |
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| container_title | Operations research |
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| creator | Peng, Yijie Fu, Michael C. Hu, Jian-Qiang Heidergott, Bernd |
| description | In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the single-run efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments.
The online appendix is available at
https://doi.org/10.1287/opre.2017.1674
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| doi_str_mv | 10.1287/opre.2017.1674 |
| format | Article |
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The online appendix is available at
https://doi.org/10.1287/opre.2017.1674
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The online appendix is available at
https://doi.org/10.1287/opre.2017.1674
.</description><subject>Analysis</subject><subject>Control charts</subject><subject>Control systems</subject><subject>discontinuous sample performance</subject><subject>Estimation theory</subject><subject>Estimators</subject><subject>Learning models (Stochastic processes)</subject><subject>Likelihood ratio</subject><subject>METHODS</subject><subject>Multivariable control systems</subject><subject>Operations research</subject><subject>Parameters</subject><subject>perturbation analysis</subject><subject>Perturbation methods</subject><subject>simulation</subject><subject>Stochastic control theory</subject><subject>stochastic derivative estimation</subject><subject>Stochastic models</subject><subject>weak derivative</subject><issn>0030-364X</issn><issn>1526-5463</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><recordid>eNqFkl1rFDEUhgdRcK3eeicEBK86azL5mMzl0tYPKFqoBe9CJnNmN8vOZM3JtPrvzbBiXViQkAROnvck5-QtiteMLlml6_dhH2FZUVYvmarFk2LBZKVKKRR_Wiwo5bTkSnx_XrxA3FJKG6nkoggr8gUeyN3YeovQkdsU3MZi8o5cQvT3Nvl7IFc5MNgUIunzvPTowpj8OIUJya0d9jsgNxDz2WBHB0gefNrkVHFyaYp2R25stAMkiPiyeNbbHcKrP_tZcffh6tvFp_L668fPF6vr0ikqU6krrhtnJbWd7BjnouXaSalEIzR1nega7XJJtmqFpFrJBnjnFDjW67at24afFW8Pefcx_JgAk9mGKY75SlMxzioqKy0fqbXdgfFjH1K0bsj1mZXkuXNc1TNVnqDWMEKuLYzQ-xw-4pcn-Dw6GLw7KXh3JJi7Cz_T2k6I5hg8_wdsJ_QjYF7QrzcJD_yph7gYECP0Zh_zP8ZfhlEze8bMnjGzZ8zsmSx4cxBsMX_2X1roWuhK6sdOzEXFAf-X7zehm8vj</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Peng, Yijie</creator><creator>Fu, Michael C.</creator><creator>Hu, Jian-Qiang</creator><creator>Heidergott, Bernd</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>XI7</scope><scope>JQ2</scope><scope>K9.</scope><orcidid>https://orcid.org/0000-0003-2584-8131</orcidid></search><sort><creationdate>20180301</creationdate><title>A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters</title><author>Peng, Yijie ; Fu, Michael C. ; Hu, Jian-Qiang ; Heidergott, Bernd</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c605t-82389ca50ad5d1334b38c55649480cd4d98c003a2b4508659e3dc6ec1f8bb7b93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Analysis</topic><topic>Control charts</topic><topic>Control systems</topic><topic>discontinuous sample performance</topic><topic>Estimation theory</topic><topic>Estimators</topic><topic>Learning models (Stochastic processes)</topic><topic>Likelihood ratio</topic><topic>METHODS</topic><topic>Multivariable control systems</topic><topic>Operations research</topic><topic>Parameters</topic><topic>perturbation analysis</topic><topic>Perturbation methods</topic><topic>simulation</topic><topic>Stochastic control theory</topic><topic>stochastic derivative estimation</topic><topic>Stochastic models</topic><topic>weak derivative</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peng, Yijie</creatorcontrib><creatorcontrib>Fu, Michael C.</creatorcontrib><creatorcontrib>Hu, Jian-Qiang</creatorcontrib><creatorcontrib>Heidergott, Bernd</creatorcontrib><collection>CrossRef</collection><collection>Gale Business: Insights</collection><collection>Business Insights: Essentials</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Peng, Yijie</au><au>Fu, Michael C.</au><au>Hu, Jian-Qiang</au><au>Heidergott, Bernd</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters</atitle><jtitle>Operations research</jtitle><date>2018-03-01</date><risdate>2018</risdate><volume>66</volume><issue>2</issue><spage>487</spage><epage>499</epage><pages>487-499</pages><issn>0030-364X</issn><eissn>1526-5463</eissn><abstract>In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the single-run efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments.
The online appendix is available at
https://doi.org/10.1287/opre.2017.1674
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| subjects | Analysis Control charts Control systems discontinuous sample performance Estimation theory Estimators Learning models (Stochastic processes) Likelihood ratio METHODS Multivariable control systems Operations research Parameters perturbation analysis Perturbation methods simulation Stochastic control theory stochastic derivative estimation Stochastic models weak derivative |
| title | A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters |
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