Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator
Computer simulators are widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modelling, and manufacturing. One fundamental aspect of the study of computer simulators is known as experimental design, that is, how to select the input settings wh...
Uloženo v:
| Vydáno v: | Journal of statistical theory and practice Ročník 14; číslo 1 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cham
Springer International Publishing
01.03.2020
|
| Témata: | |
| ISSN: | 1559-8608, 1559-8616 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Computer simulators are widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modelling, and manufacturing. One fundamental aspect of the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected. Extra care should be taken in the selection process because computer simulators can be computationally expensive to run. The selection should acknowledge and achieve the goal of the analysis. This article focuses on the goal of producing more accurate prediction which is important for risk assessment and decision making. We propose two new methods of design approaches that sequentially select input settings to achieve this goal. The approaches make novel applications of simultaneous and sequential contour estimations. Numerical examples are employed to demonstrate the effectiveness of the proposed approaches. |
|---|---|
| AbstractList | Computer simulators are widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modelling, and manufacturing. One fundamental aspect of the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected. Extra care should be taken in the selection process because computer simulators can be computationally expensive to run. The selection should acknowledge and achieve the goal of the analysis. This article focuses on the goal of producing more accurate prediction which is important for risk assessment and decision making. We propose two new methods of design approaches that sequentially select input settings to achieve this goal. The approaches make novel applications of simultaneous and sequential contour estimations. Numerical examples are employed to demonstrate the effectiveness of the proposed approaches. |
| ArticleNumber | 9 |
| Author | Ranjan, P. Yang, F. Lin, C. Devon |
| Author_xml | – sequence: 1 givenname: F. surname: Yang fullname: Yang, F. organization: College of Mathematics, Sichuan University – sequence: 2 givenname: C. Devon orcidid: 0000-0002-5027-7361 surname: Lin fullname: Lin, C. Devon email: devon.lin@queensu.ca organization: Department of Mathematics and Statistics, Queen’s University – sequence: 3 givenname: P. surname: Ranjan fullname: Ranjan, P. organization: OM&QT Area, Indian Institute of Management Indore |
| BookMark | eNp9kEFOwzAQRS1UJErhAOx8gYDtOHGyRFVbkIqQKLC1HGdcXKVxZTtI3B6HIhYsuviaWcybmf8v0aR3PSB0Q8ktJUTcBc4KWmdkFBEiI2doSouizqqSlpO_nlQX6DKEHSElJXk-Re-rzjWqw0sbo-232BkcPwC_QDi4PgDeDN4oDfjTKrwI0e7Vz9jT0EV76ADPXR_d4MMIKryx-6FT0fkrdG5UF-D6t87Q23LxOn_I1s-rx_n9OtOsqmKmdMEgN4VphObQNoKS1LeMG86Z4aYVwLg2VAC0JdRGaF1pWusiF6Y1rMlniB73au9C8GDkwacf_ZekRI7ByGMwkoxKwUiSGPGP0TYmW8mJV7Y7SbIjGdKVfgte7pL3Phk8AX0DDW57xg |
| CitedBy_id | crossref_primary_10_1080_00224065_2023_2241680 crossref_primary_10_1007_s00366_021_01516_2 |
| Cites_doi | 10.1002/cjs.11156 10.1201/9781420034899 10.1198/TECH.2009.08040 10.1093/biomet/asv002 10.1214/17-AOS1648 10.1198/004017006000000228 10.1080/10618600.2018.1473778 10.1007/978-1-4757-3799-8 10.1016/j.jspi.2009.12.004 10.1007/978-1-4612-3818-8_32 10.1080/00401706.2014.881749 10.1111/j.1365-2966.2006.10519.x 10.1080/03610918208812265 10.1198/TECH.2009.08018 10.1198/004017008000000541 10.1007/s11222-010-9224-x 10.1198/TECH.2009.0015 10.1080/01621459.1991.10475138 10.1198/TECH.2010.09157 10.18637/jss.v064.i13 10.1080/00401706.2016.1211554 10.1080/00401706.1989.10488474 10.1080/15210607909379345 10.1080/01621459.1993.10476423 10.1016/0378-3758(90)90122-B 10.1198/016214508000000689 10.1016/0378-3758(94)00035-T 10.1214/ss/1177012413 |
| ContentType | Journal Article |
| Copyright | Grace Scientific Publishing 2019 |
| Copyright_xml | – notice: Grace Scientific Publishing 2019 |
| DBID | AAYXX CITATION |
| DOI | 10.1007/s42519-019-0077-0 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics Mathematics |
| EISSN | 1559-8616 |
| ExternalDocumentID | 10_1007_s42519_019_0077_0 |
| GrantInformation_xml | – fundername: University Postgraduate Programme funderid: http://dx.doi.org/10.13039/501100010907 – fundername: Natural Sciences and Engineering Research Council of Canada funderid: http://dx.doi.org/10.13039/501100000038 – fundername: Department of Science and Technology, Government of India grantid: EMR/2016/003332/MS |
| GroupedDBID | -EM .7F 0R~ 4.4 406 8UJ AACDK AAHNG AAJBT AASML AATNV AAUYE ABAKF ABECU ABFIM ABFTV ABJNI ABKCH ABMQK ABPEM ABTAI ABTEG ABTKH ABTMW ABXPI ACAOD ACDTI ACGFS ACHSB ACMLO ACOKC ACPIV ACTIO ACZOJ ADCVX ADKNI ADTPH ADURQ ADYFF AEFQL AEJRE AEMSY AESKC AFBBN AFQWF AGDGC AGJBK AGMZJ AGQEE AIGIU AIJEM AILAN AITGF AJZVZ ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AQRUH AVBZW AXYYD BGNMA CCCUG CSCUP DKSSO DPUIP EBLON EBS EJD E~A E~B F5P FIGPU FINBP FNLPD FSGXE GGCAI GTTXZ H13 HZ~ H~P IKXTQ IWAJR J9A JZLTJ J~4 KOV LLZTM M4Y M4Z NPVJJ NQJWS NU0 O9- P2P PT4 ROL RSV S-T SJN SJYHP SNE SNPRN SOHCF SOJ SRMVM SSLCW STPWE TDBHL TFL TFT TFW TSG UOJIU UT5 UTJUX UU3 VEKWB VFIZW ZMTXR AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA AZQEC BENPR CCPQU CITATION DWQXO GNUQQ GUQSH HCIFZ M2O M2P PHGZM PHGZT |
| ID | FETCH-LOGICAL-c288t-ac52e3f5fb7c4edb7105fbd24f442f4fd7e24cf17eed6e9f7cc8c19c537fdf2b3 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000511595500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1559-8608 |
| IngestDate | Tue Nov 18 22:34:25 EST 2025 Sat Nov 29 01:41:36 EST 2025 Fri Feb 21 02:31:16 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Computer experiment Gaussian process Sequential design Maximin design Contour estimation Latin hypercube Space-filling |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c288t-ac52e3f5fb7c4edb7105fbd24f442f4fd7e24cf17eed6e9f7cc8c19c537fdf2b3 |
| ORCID | 0000-0002-5027-7361 |
| ParticipantIDs | crossref_primary_10_1007_s42519_019_0077_0 crossref_citationtrail_10_1007_s42519_019_0077_0 springer_journals_10_1007_s42519_019_0077_0 |
| PublicationCentury | 2000 |
| PublicationDate | 20200300 |
| PublicationDateYYYYMMDD | 2020-03-01 |
| PublicationDate_xml | – month: 3 year: 2020 text: 20200300 |
| PublicationDecade | 2020 |
| PublicationPlace | Cham |
| PublicationPlace_xml | – name: Cham |
| PublicationTitle | Journal of statistical theory and practice |
| PublicationTitleAbbrev | J Stat Theory Pract |
| PublicationYear | 2020 |
| Publisher | Springer International Publishing |
| Publisher_xml | – name: Springer International Publishing |
| References | Dancik G (2018) mlegp: maximum likelihood estimates of Gaussian processes. R package version 3.1.7 ZhangRLinCDRanjanPLocal approximate Gaussian process model for large-scale dynamic computer experimentsJ Comput Graph Stat20182779880710.1080/10618600.2018.1473778 TangBOrthogonal array-based Latin hypercubesJ Am Stat Assoc19938813921397124537510.1080/01621459.1993.10476423 RasmussenCEWilliamsCKIGaussian processes for machine learning2006Cambridge, MAThe MIT Press1177.68165 FangKTLiRSudjiantoADesign and modeling for computer experiments2005New YorkCRC Press10.1201/9781420034899 BowerRGBensonAJThe broken hierarchy of galaxy formationMon Not R Astron Soc200637064565510.1111/j.1365-2966.2006.10519.x OwenABOrthogonal arrays for computer experiments, integration and visualizationStat Sin1992243945211879520822.62064 JohnsonMMooreLYlvisakerDMinimax and maximin distance designJ Stat Plan Inference199026131148107925810.1016/0378-3758(90)90122-B MorrisMDMitchellTJExploratory designs for computer experimentsJ Stat Plan Inference19954338140210.1016/0378-3758(94)00035-T GramacyRBLeeHKHBayesian treed Gaussian process models with an application to computer modelingJ Am Stat Assoc200810348311191130252883010.1198/016214508000000689 Ba S (2015) SLHD: maximin-distance (sliced) Latin hypercube designs. R package version 2.1-1 GramacyRBLeeHKHAdaptive design and analysis of supercomputer experimentsTechnometrics2009512130145266817010.1198/TECH.2009.0015 ChipmanHRanjanPWangWSequential design for computer experiments with a flexible Bayesian additive modelCan J Stat2012404663678299885510.1002/cjs.11156 LoeppkyJLMooreLMWilliamsBJBatch sequential designs for computer experimentsJ Stat Plan Inference2010140614521464259222410.1016/j.jspi.2009.12.004 McKayMDBeckmanRJConoverWJA comparison of three methods for selecting values of input variables in the analysis of output from a computer codeTechnometrics197921239455332520415.62011 GramacyRBLeeHKCases for the nugget in modeling computer experimentsStat Comput2012223713722290961710.1007/s11222-010-9224-x JosephVRDasguptaTTuoRWuCJSequential exploration of complex surfaces using minimum energy designsTechnometrics2015576474331835010.1080/00401706.2014.881749 SacksJWelchWJMitchellTJWynnHPDesign and analysis of computer experimentsStat Sci19894409423104176510.1214/ss/1177012413 DetteHPepelyshevAGeneralized Latin hypercube design for computer experimentsTechnometrics201052421429278924910.1198/TECH.2010.09157 GreenbergDA numerical model investigation of tidal phenomena in the Bay of Fundy and Gulf of MaineMar Geod1979216118710.1080/15210607909379345 Gramacy RB, Taddy MA (2016) tgp: Bayesian treed Gaussian process models. R package version 2-4-14 LamCQNotzWISequential adaptive designs in computer experiments for response surface model fitStat Appl20086207233 RanjanPBinghamDMichailidisGSequential experiment design for contour estimation from complex computer codesTechnometrics2008504527541265565110.1198/004017008000000541(Errata (2011), Technometrics, 53:1, 109–110) SacksJSchillerSBWelchWJDesigns for computer experimentsTechnometrics198931414799766910.1080/00401706.1989.10488474 SacksJSchillerSGuptaBergerSpatial designsStatistical decision theory and related topics IV1988New YorkSpringer38539910.1007/978-1-4612-3818-8_32 PalomoJPauloRGarcia-DonatoGSAVE: an r package for the statistical analysis of computer modelsJ Stat Softw2015641312310.18637/jss.v064.i13 Gu M, Palomo J, Berger J (2018) RobustGaSP: robust Gaussian stochastic process emulation. R package version 0.5.6 LinkletterCBinghamDHengartnerNHigdonDYeKQVariable selection for Gaussian process models in computer experimentsTechnometrics2006484478490232861710.1198/004017006000000228 CurrinCMitchellTMorrisMYlvisakerDBayesian prediction of deterministic functions, with applications to the design and analysis of computer experimentsJ Am Stat Assoc199186416953963114634310.1080/01621459.1991.10475138 SantnerTJWilliamsBJNotzWIThe design and analysis of computer experiments2003New YorkSpringer10.1007/978-1-4757-3799-8 LoeppkyJLSacksJWelchWJChoosing the sample size of a computer experiment: a practical guideTechnometrics2009514366376275647310.1198/TECH.2009.08040 JosephVRGulEBaSMaximum projection designs for computer experimentsBiometrika2015102371380337101010.1093/biomet/asv002 DengXLinCDLiuKWRoweRKAdditive Gaussian process for computer models with qualitative and quantitative factorsTechnometrics201759283292367796010.1080/00401706.2016.1211554 BayarriMJBergerJOCalderESDalbeyKLunagomezSPatraAKPitmanEBSpillerETWolpertRLUsing statistical and computer models to quantify volcanic hazardsTechnometrics200951402413275647610.1198/TECH.2009.08018 DixonLCWSzegoGPThe global optimization problem: an introductionTowards Glob Optim19782115 Roustant O, Ginsbourger D, Deville Y (2018) DiceKriging: kriging methods for computer experiments. R package version 1.5.6 MacDonald B, Ranjan P, Chipman H (2015) GPfit: an R package for Gaussian process model fitting using a new optimization algorithm. R package version 1.0-1 ImanRLConoverWJA distribution-free approach to inducing rank correlation among input variablesCommun Stat Part B Simul Comput19821131133410.1080/03610918208812265 JosephVRHungYOrthogonal-maximin Latin hypercube designsStat Sin20081817118624169071137.62050 Park JS (1991) Tuning complex computer codes to data and optimal designs. Unpublished Ph.D. thesis, University of Illinois, Champaign-Urbana RB Gramacy (77_CR11) 2008; 103 C Linkletter (77_CR23) 2006; 48 M Johnson (77_CR18) 1990; 26 C Currin (77_CR5) 1991; 86 77_CR6 AB Owen (77_CR29) 1992; 2 J Palomo (77_CR30) 2015; 64 JL Loeppky (77_CR24) 2010; 140 77_CR1 D Greenberg (77_CR15) 1979; 2 MJ Bayarri (77_CR2) 2009; 51 77_CR26 RG Bower (77_CR3) 2006; 370 X Deng (77_CR7) 2017; 59 H Dette (77_CR8) 2010; 52 VR Joseph (77_CR20) 2015; 102 R Zhang (77_CR40) 2018; 27 RL Iman (77_CR17) 1982; 11 CQ Lam (77_CR22) 2008; 6 VR Joseph (77_CR21) 2008; 18 RB Gramacy (77_CR12) 2009; 51 LCW Dixon (77_CR9) 1978; 2 P Ranjan (77_CR33) 2008; 50 77_CR32 77_CR31 J Sacks (77_CR37) 1988 MD McKay (77_CR27) 1979; 21 J Sacks (77_CR38) 1989; 4 CE Rasmussen (77_CR34) 2006 VR Joseph (77_CR19) 2015; 57 B Tang (77_CR39) 1993; 88 JL Loeppky (77_CR25) 2009; 51 TJ Santner (77_CR35) 2003 KT Fang (77_CR10) 2005 77_CR16 RB Gramacy (77_CR13) 2012; 22 77_CR14 H Chipman (77_CR4) 2012; 40 MD Morris (77_CR28) 1995; 43 J Sacks (77_CR36) 1989; 31 |
| References_xml | – reference: Dancik G (2018) mlegp: maximum likelihood estimates of Gaussian processes. R package version 3.1.7 – reference: PalomoJPauloRGarcia-DonatoGSAVE: an r package for the statistical analysis of computer modelsJ Stat Softw2015641312310.18637/jss.v064.i13 – reference: BowerRGBensonAJThe broken hierarchy of galaxy formationMon Not R Astron Soc200637064565510.1111/j.1365-2966.2006.10519.x – reference: DengXLinCDLiuKWRoweRKAdditive Gaussian process for computer models with qualitative and quantitative factorsTechnometrics201759283292367796010.1080/00401706.2016.1211554 – reference: GreenbergDA numerical model investigation of tidal phenomena in the Bay of Fundy and Gulf of MaineMar Geod1979216118710.1080/15210607909379345 – reference: DixonLCWSzegoGPThe global optimization problem: an introductionTowards Glob Optim19782115 – reference: Gu M, Palomo J, Berger J (2018) RobustGaSP: robust Gaussian stochastic process emulation. R package version 0.5.6 – reference: MorrisMDMitchellTJExploratory designs for computer experimentsJ Stat Plan Inference19954338140210.1016/0378-3758(94)00035-T – reference: Park JS (1991) Tuning complex computer codes to data and optimal designs. Unpublished Ph.D. thesis, University of Illinois, Champaign-Urbana – reference: LoeppkyJLMooreLMWilliamsBJBatch sequential designs for computer experimentsJ Stat Plan Inference2010140614521464259222410.1016/j.jspi.2009.12.004 – reference: JosephVRGulEBaSMaximum projection designs for computer experimentsBiometrika2015102371380337101010.1093/biomet/asv002 – reference: GramacyRBLeeHKCases for the nugget in modeling computer experimentsStat Comput2012223713722290961710.1007/s11222-010-9224-x – reference: Roustant O, Ginsbourger D, Deville Y (2018) DiceKriging: kriging methods for computer experiments. R package version 1.5.6 – reference: ImanRLConoverWJA distribution-free approach to inducing rank correlation among input variablesCommun Stat Part B Simul Comput19821131133410.1080/03610918208812265 – reference: JosephVRDasguptaTTuoRWuCJSequential exploration of complex surfaces using minimum energy designsTechnometrics2015576474331835010.1080/00401706.2014.881749 – reference: TangBOrthogonal array-based Latin hypercubesJ Am Stat Assoc19938813921397124537510.1080/01621459.1993.10476423 – reference: OwenABOrthogonal arrays for computer experiments, integration and visualizationStat Sin1992243945211879520822.62064 – reference: GramacyRBLeeHKHBayesian treed Gaussian process models with an application to computer modelingJ Am Stat Assoc200810348311191130252883010.1198/016214508000000689 – reference: SantnerTJWilliamsBJNotzWIThe design and analysis of computer experiments2003New YorkSpringer10.1007/978-1-4757-3799-8 – reference: BayarriMJBergerJOCalderESDalbeyKLunagomezSPatraAKPitmanEBSpillerETWolpertRLUsing statistical and computer models to quantify volcanic hazardsTechnometrics200951402413275647610.1198/TECH.2009.08018 – reference: GramacyRBLeeHKHAdaptive design and analysis of supercomputer experimentsTechnometrics2009512130145266817010.1198/TECH.2009.0015 – reference: ZhangRLinCDRanjanPLocal approximate Gaussian process model for large-scale dynamic computer experimentsJ Comput Graph Stat20182779880710.1080/10618600.2018.1473778 – reference: RanjanPBinghamDMichailidisGSequential experiment design for contour estimation from complex computer codesTechnometrics2008504527541265565110.1198/004017008000000541(Errata (2011), Technometrics, 53:1, 109–110) – reference: Gramacy RB, Taddy MA (2016) tgp: Bayesian treed Gaussian process models. R package version 2-4-14 – reference: JohnsonMMooreLYlvisakerDMinimax and maximin distance designJ Stat Plan Inference199026131148107925810.1016/0378-3758(90)90122-B – reference: LamCQNotzWISequential adaptive designs in computer experiments for response surface model fitStat Appl20086207233 – reference: SacksJSchillerSGuptaBergerSpatial designsStatistical decision theory and related topics IV1988New YorkSpringer38539910.1007/978-1-4612-3818-8_32 – reference: SacksJWelchWJMitchellTJWynnHPDesign and analysis of computer experimentsStat Sci19894409423104176510.1214/ss/1177012413 – reference: CurrinCMitchellTMorrisMYlvisakerDBayesian prediction of deterministic functions, with applications to the design and analysis of computer experimentsJ Am Stat Assoc199186416953963114634310.1080/01621459.1991.10475138 – reference: MacDonald B, Ranjan P, Chipman H (2015) GPfit: an R package for Gaussian process model fitting using a new optimization algorithm. R package version 1.0-1 – reference: RasmussenCEWilliamsCKIGaussian processes for machine learning2006Cambridge, MAThe MIT Press1177.68165 – reference: DetteHPepelyshevAGeneralized Latin hypercube design for computer experimentsTechnometrics201052421429278924910.1198/TECH.2010.09157 – reference: Ba S (2015) SLHD: maximin-distance (sliced) Latin hypercube designs. R package version 2.1-1 – reference: JosephVRHungYOrthogonal-maximin Latin hypercube designsStat Sin20081817118624169071137.62050 – reference: McKayMDBeckmanRJConoverWJA comparison of three methods for selecting values of input variables in the analysis of output from a computer codeTechnometrics197921239455332520415.62011 – reference: LinkletterCBinghamDHengartnerNHigdonDYeKQVariable selection for Gaussian process models in computer experimentsTechnometrics2006484478490232861710.1198/004017006000000228 – reference: LoeppkyJLSacksJWelchWJChoosing the sample size of a computer experiment: a practical guideTechnometrics2009514366376275647310.1198/TECH.2009.08040 – reference: SacksJSchillerSBWelchWJDesigns for computer experimentsTechnometrics198931414799766910.1080/00401706.1989.10488474 – reference: ChipmanHRanjanPWangWSequential design for computer experiments with a flexible Bayesian additive modelCan J Stat2012404663678299885510.1002/cjs.11156 – reference: FangKTLiRSudjiantoADesign and modeling for computer experiments2005New YorkCRC Press10.1201/9781420034899 – volume: 40 start-page: 663 issue: 4 year: 2012 ident: 77_CR4 publication-title: Can J Stat doi: 10.1002/cjs.11156 – volume: 18 start-page: 171 year: 2008 ident: 77_CR21 publication-title: Stat Sin – volume-title: Design and modeling for computer experiments year: 2005 ident: 77_CR10 doi: 10.1201/9781420034899 – volume: 51 start-page: 366 issue: 4 year: 2009 ident: 77_CR25 publication-title: Technometrics doi: 10.1198/TECH.2009.08040 – volume: 102 start-page: 371 year: 2015 ident: 77_CR20 publication-title: Biometrika doi: 10.1093/biomet/asv002 – ident: 77_CR6 – ident: 77_CR16 doi: 10.1214/17-AOS1648 – ident: 77_CR32 – volume-title: Gaussian processes for machine learning year: 2006 ident: 77_CR34 – volume: 48 start-page: 478 issue: 4 year: 2006 ident: 77_CR23 publication-title: Technometrics doi: 10.1198/004017006000000228 – volume: 2 start-page: 439 year: 1992 ident: 77_CR29 publication-title: Stat Sin – volume: 27 start-page: 798 year: 2018 ident: 77_CR40 publication-title: J Comput Graph Stat doi: 10.1080/10618600.2018.1473778 – volume-title: The design and analysis of computer experiments year: 2003 ident: 77_CR35 doi: 10.1007/978-1-4757-3799-8 – volume: 140 start-page: 1452 issue: 6 year: 2010 ident: 77_CR24 publication-title: J Stat Plan Inference doi: 10.1016/j.jspi.2009.12.004 – start-page: 385 volume-title: Statistical decision theory and related topics IV year: 1988 ident: 77_CR37 doi: 10.1007/978-1-4612-3818-8_32 – volume: 2 start-page: 1 year: 1978 ident: 77_CR9 publication-title: Towards Glob Optim – volume: 57 start-page: 64 year: 2015 ident: 77_CR19 publication-title: Technometrics doi: 10.1080/00401706.2014.881749 – volume: 370 start-page: 645 year: 2006 ident: 77_CR3 publication-title: Mon Not R Astron Soc doi: 10.1111/j.1365-2966.2006.10519.x – ident: 77_CR1 – volume: 11 start-page: 311 year: 1982 ident: 77_CR17 publication-title: Commun Stat Part B Simul Comput doi: 10.1080/03610918208812265 – volume: 51 start-page: 402 year: 2009 ident: 77_CR2 publication-title: Technometrics doi: 10.1198/TECH.2009.08018 – volume: 50 start-page: 527 issue: 4 year: 2008 ident: 77_CR33 publication-title: Technometrics doi: 10.1198/004017008000000541 – ident: 77_CR14 – ident: 77_CR31 – volume: 22 start-page: 713 issue: 3 year: 2012 ident: 77_CR13 publication-title: Stat Comput doi: 10.1007/s11222-010-9224-x – volume: 51 start-page: 130 issue: 2 year: 2009 ident: 77_CR12 publication-title: Technometrics doi: 10.1198/TECH.2009.0015 – volume: 21 start-page: 239 year: 1979 ident: 77_CR27 publication-title: Technometrics – volume: 86 start-page: 953 issue: 416 year: 1991 ident: 77_CR5 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1991.10475138 – volume: 52 start-page: 421 year: 2010 ident: 77_CR8 publication-title: Technometrics doi: 10.1198/TECH.2010.09157 – volume: 64 start-page: 1 issue: 13 year: 2015 ident: 77_CR30 publication-title: J Stat Softw doi: 10.18637/jss.v064.i13 – volume: 59 start-page: 283 year: 2017 ident: 77_CR7 publication-title: Technometrics doi: 10.1080/00401706.2016.1211554 – volume: 31 start-page: 41 year: 1989 ident: 77_CR36 publication-title: Technometrics doi: 10.1080/00401706.1989.10488474 – volume: 2 start-page: 161 year: 1979 ident: 77_CR15 publication-title: Mar Geod doi: 10.1080/15210607909379345 – volume: 88 start-page: 1392 year: 1993 ident: 77_CR39 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1993.10476423 – volume: 26 start-page: 131 year: 1990 ident: 77_CR18 publication-title: J Stat Plan Inference doi: 10.1016/0378-3758(90)90122-B – ident: 77_CR26 – volume: 103 start-page: 1119 issue: 483 year: 2008 ident: 77_CR11 publication-title: J Am Stat Assoc doi: 10.1198/016214508000000689 – volume: 43 start-page: 381 year: 1995 ident: 77_CR28 publication-title: J Stat Plan Inference doi: 10.1016/0378-3758(94)00035-T – volume: 4 start-page: 409 year: 1989 ident: 77_CR38 publication-title: Stat Sci doi: 10.1214/ss/1177012413 – volume: 6 start-page: 207 year: 2008 ident: 77_CR22 publication-title: Stat Appl |
| SSID | ssj0061033 |
| Score | 2.1568038 |
| Snippet | Computer simulators are widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modelling, and... |
| SourceID | crossref springer |
| SourceType | Enrichment Source Index Database Publisher |
| SubjectTerms | Algorithms Analysis and Advanced Methodologies in the Design of Experiments Mathematics and Statistics Original Article Probability Theory and Stochastic Processes Statistical Theory and Methods Statistics |
| Title | Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator |
| URI | https://link.springer.com/article/10.1007/s42519-019-0077-0 |
| Volume | 14 |
| WOSCitedRecordID | wos000511595500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1559-8616 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0061033 issn: 1559-8608 databaseCode: RSV dateStart: 20070301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature – providerCode: PRVAWR databaseName: Taylor & Francis Journals Complete customDbUrl: eissn: 1559-8616 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0061033 issn: 1559-8608 databaseCode: TFW dateStart: 20070301 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LSwMxEA5SPdSDj6pYX-TgSQlks9lNehRp8WCLtFp6W7LZBAraym7b3-8k-4CCCnrIsoeZbMhkMzPMzDcI3QbKCOG8E0alJhzuRpKGNCPShAFwcDBBvKSfxWgkZ7PeS1XHXdTZ7nVI0t_UTbEbd0WW4PrCoEIQ8NN3QdtJ169hPJnW1y-YA75_vAu3ERlTWYcyv5tiWxltR0K9ghkc_mtpR-igsifxQ3kAjtGOWXTQ_rABYy06qO0MyhKP-QRNS5B_PJj7hGe8tBhI8bhMlTV4ss6t0gZv5gr3gcdNAmTDKu8QOzQr-GbhGBWezD9c-69lforeBv3XxydS9VYgmkm5IkpHzIQ2sqnQ3GQpGBrwnjFuOWeW20wYxrUNBOjQ2PSs0FrqoKejUNjMsjQ8Q63FcmHOETbKZtxEEVMm5uBwKGrSOJVcRe4pZBfRepMTXQGPu_4X70kDmez3L6FuUIc93kV3DctnibrxG_F9LZWk-gGLn6kv_kR9idrMOdg-6ewKtVb52lyjPb0BseU3_uB9AVwK0uQ |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwEA8yBeeDH1NxfubBJ6WQpmmTPYpsTNyGbHPsraRpAgPdZN3293tJP0BQQR9S-nCXhlyau-PufofQrS8159Y7oUQoj8Hd6CUBST2hAx84GJggTtI9PhiI6bT1UtRxZ2W2exmSdDd1VezGbJEluL4wCOce-OnbDBSWBcwfjibl9QvmgOsfb8NtnoiIKEOZ303xVRl9jYQ6BdM5-NfSDtF-YU_ih_wAHKEtPW-gvX4Fxpo1UN0alDke8zGa5CD_uDNzCc94YTCQ4mGeKqvxaL00Umm8mUncBh47CZD1i7xDbNGs4JuZZZR4NHu37b8WyxP02mmPH7te0VvBU1SIlSdVSHVgQpNwxXSagKEB7yllhjFqmEm5pkwZn4MOjXTLcKWE8lsqDLhJDU2CU1SbL-b6DGEtTcp0GFKpIwYOhyQ6iRLBZGifXDQRKTc5VgXwuO1_8RZXkMlu_2JiB7HY4010V7F85KgbvxHfl1KJix8w-5n6_E_UN2i3O-734t7T4PkC1al1tl0C2iWqrZZrfYV21AZEuLx2h_ATSI_VyA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwEA8yRfTBj6k4P_PgkxKWpmmTPYquKG5jOB17K2mawEC70W77-036BQMVxIeUPtylIZfm7ri73wFw4wjFmPVOCOYSUXM3osjFMeLKdQwHNSZILukeGwz4ZNIZln1OsyrbvQpJFjUNFqUpWbTnsW7XhW_UFlwaN9gMzBgyPvsmtXn01l0fjaur2JgGeS95G3pD3Me8Cmt-N8W6YlqPiubKJtj_9zIPwF5pZ8L74mAcgg2VNMFuvwZpzZpgxxqaBU7zERgX4P8wmOaJ0HCmoSGFr0UKrYKjZaqFVHA1FbBreOwkhqxf5iNCi3JlvplZRgFH00_bFmyWHoP3oPv28ITKngtIEs4XSEiPKFd7OmKSqjgyBoh5jwnVlBJNdcwUoVI7zOhWX3U0k5JLpyM9l-lYk8g9AY1klqhTAJXQMVWeR4TyqXFEBFaRH3EqPPtkvAVwteGhLAHJbV-Mj7CGUs73L8R2YItJ3gK3Ncu8QOP4jfiuklBY_pjZz9Rnf6K-BtvDxyDsPQ9ezsEOsT54npd2ARqLdKkuwZZcGQmmV_l5_AJ4FN6s |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Global+Fitting+of+the+Response+Surface+via+Estimating+Multiple+Contours+of+a+Simulator&rft.jtitle=Journal+of+statistical+theory+and+practice&rft.au=Yang%2C+F.&rft.au=Lin%2C+C.+Devon&rft.au=Ranjan%2C+P.&rft.date=2020-03-01&rft.pub=Springer+International+Publishing&rft.issn=1559-8608&rft.eissn=1559-8616&rft.volume=14&rft.issue=1&rft_id=info:doi/10.1007%2Fs42519-019-0077-0&rft.externalDocID=10_1007_s42519_019_0077_0 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1559-8608&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1559-8608&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1559-8608&client=summon |