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DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

Citations

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Cited by:

  1. Ruimeng Hu & Mike Ludkovski, 2015. "Sequential Design for Ranking Response Surfaces," Papers 1509.00980, arXiv.org, revised Jul 2016.
  2. Torossian, Léonard & Picheny, Victor & Faivre, Robert & Garivier, Aurélien, 2020. "A review on quantile regression for stochastic computer experiments," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
  3. Ehsan Mehdad & Jack P. C. Kleijnen, 2018. "Efficient global optimisation for black-box simulation via sequential intrinsic Kriging," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1725-1737, November.
  4. Wu, Xu & Kozlowski, Tomasz & Meidani, Hadi, 2018. "Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 422-436.
  5. Kleijnen, Jack P.C. & Mehdad, E., 2014. "Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039)," Discussion Paper 2014-012, Tilburg University, Center for Economic Research.
  6. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
  7. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Discussion Paper 2014-063, Tilburg University, Center for Economic Research.
  8. Djibril Gueye & Kokulo Lawuobahsumo, 2023. "A Probabilistic Approach for Denoising Option Prices," International Journal of Economics and Financial Issues, Econjournals, vol. 13(2), pages 18-26, March.
  9. Balata, Alessandro & Ludkovski, Michael & Maheshwari, Aditya & Palczewski, Jan, 2021. "Statistical learning for probability-constrained stochastic optimal control," European Journal of Operational Research, Elsevier, vol. 290(2), pages 640-656.
  10. Binois, M. & Ginsbourger, D. & Roustant, O., 2015. "Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations," European Journal of Operational Research, Elsevier, vol. 243(2), pages 386-394.
  11. Biewen, Martin & Kugler, Philipp, 2021. "Two-stage least squares random forests with an application to Angrist and Evans (1998)," Economics Letters, Elsevier, vol. 204(C).
  12. Risk, J. & Ludkovski, M., 2016. "Statistical emulators for pricing and hedging longevity risk products," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 45-60.
  13. G. Dosi & M. C. Pereira & M. E. Virgillito, 2018. "On the robustness of the fat-tailed distribution of firm growth rates: a global sensitivity analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 173-193, April.
  14. Kleijnen, Jack P.C. & Mehdad, E. & van Beers, W.C.M., 2012. "Convex and monotonic bootstrapped kriging," Other publications TiSEM 972e079d-0209-45bf-b25e-a, Tilburg University, School of Economics and Management.
  15. Mostafa Reisi Gahrooei & Hao Yan & Kamran Paynabar, 2020. "Comments on: On Active Learning Methods for Manifold Data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 38-41, March.
  16. Cousin, Areski & Maatouk, Hassan & Rullière, Didier, 2016. "Kriging of financial term-structures," European Journal of Operational Research, Elsevier, vol. 255(2), pages 631-648.
  17. François Bachoc & Céline Helbert & Victor Picheny, 2020. "Gaussian process optimization with failures: classification and convergence proof," Journal of Global Optimization, Springer, vol. 78(3), pages 483-506, November.
  18. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
  19. Leonardo Bargigli & Luca Riccetti & Alberto Russo & Mauro Gallegati, 2020. "Network calibration and metamodeling of a financial accelerator agent based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(2), pages 413-440, April.
  20. Palomo, Jesús & Paulo, Rui & García-Donato, Gonzalo, 2015. "SAVE: An R Package for the Statistical Analysis of Computer Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i13).
  21. Kamiński, Bogumił, 2015. "A method for the updating of stochastic kriging metamodels," European Journal of Operational Research, Elsevier, vol. 247(3), pages 859-866.
  22. Kleijnen, Jack P.C., 2013. "Simulation-Optimization via Kriging and Bootstrapping : A Survey (Revision of CentER DP 2011-064)," Other publications TiSEM 6ac4e049-ad86-447f-aeec-a, Tilburg University, School of Economics and Management.
  23. Kleijnen, J.P.C. & Mehdad, Ehsan, 2015. "Estimating the Variance of the Predictor in Stochastic Kriging," Discussion Paper 2015-041, Tilburg University, Center for Economic Research.
  24. Dupuy, Delphine & Helbert, Céline & Franco, Jessica, 2015. "DiceDesign and DiceEval: Two R Packages for Design and Analysis of Computer Experiments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i11).
  25. Mehdad, E., 2015. "Kriging metamodels and global opimization in simulation," Other publications TiSEM 5b5c276a-fe68-4ce9-b8a8-1, Tilburg University, School of Economics and Management.
  26. Xiao, Qian & Xu, Hongquan, 2021. "A mapping-based universal Kriging model for order-of-addition experiments in drug combination studies," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
  27. Zhen Liu & Ye Guo & Shuheng Du & Gengyu Wu & Mao Pan, 2017. "Research on calibrating rock mechanical parameters with a statistical method," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-16, May.
  28. Diariétou Sambakhé & Lauriane Rouan & Jean-Noël Bacro & Eric Gozé, 2019. "Conditional optimization of a noisy function using a kriging metamodel," Journal of Global Optimization, Springer, vol. 73(3), pages 615-636, March.
  29. Jack P. C. Kleijnen & Wim C. M. van Beers, 2022. "Statistical Tests for Cross-Validation of Kriging Models," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 607-621, January.
  30. Michael Ludkovski & James Risk, 2017. "Sequential Design and Spatial Modeling for Portfolio Tail Risk Measurement," Papers 1710.05204, arXiv.org, revised May 2018.
  31. Betancourt, José & Bachoc, François & Klein, Thierry & Idier, Déborah & Pedreros, Rodrigo & Rohmer, Jérémy, 2020. "Gaussian process metamodeling of functional-input code for coastal flood hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
  32. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
  33. Kugler, Philipp & Biewen, Martin, 2020. "Two-Stage Least Squares Random Forests with a Replication of Angrist and Evans (1998)," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224538, Verein für Socialpolitik / German Economic Association.
  34. Krityakierne, Tipaluck & Baowan, Duangkamon, 2020. "Aggregated GP-based Optimization for Contaminant Source Localization," Operations Research Perspectives, Elsevier, vol. 7(C).
  35. Thomas Welchowski & Matthias Schmid, 2019. "Sparse kernel deep stacking networks," Computational Statistics, Springer, vol. 34(3), pages 993-1014, September.
  36. Li, Peiping & Wang, Yu, 2022. "An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS)," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
  37. Schlather, Martin & Malinowski, Alexander & Menck, Peter J. & Oesting, Marco & Strokorb, Kirstin, 2015. "Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i08).
  38. Youssef Diouane & Victor Picheny & Rodolophe Le Riche & Alexandre Scotto Di Perrotolo, 2023. "TREGO: a trust-region framework for efficient global optimization," Journal of Global Optimization, Springer, vol. 86(1), pages 1-23, May.
  39. Loïc Iapteff & Julien Jacques & Matthieu Rolland & Benoit Celse, 2021. "Reducing the number of experiments required for modelling the hydrocracking process with kriging through Bayesian transfer learning," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1344-1364, November.
  40. C Rohrbeck & D A Costain & A Frigessi, 2018. "Bayesian spatial monotonic multiple regression," Biometrika, Biometrika Trust, vol. 105(3), pages 691-707.
  41. Mohammadi, Hossein & Challenor, Peter & Goodfellow, Marc, 2019. "Emulating dynamic non-linear simulators using Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 178-196.
  42. Mike Ludkovski, 2020. "mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms," Papers 2012.00729, arXiv.org, revised Oct 2022.
  43. Alessandro Balata & Michael Ludkovski & Aditya Maheshwari & Jan Palczewski, 2019. "Statistical Learning for Probability-Constrained Stochastic Optimal Control," Papers 1905.00107, arXiv.org, revised Aug 2020.
  44. Cousin, Areski & Maatouk, Hassan & Rullière, Didier, 2016. "Kriging of financial term-structures," European Journal of Operational Research, Elsevier, vol. 255(2), pages 631-648.
  45. Fruth, J. & Roustant, O. & Kuhnt, S., 2019. "Support indices: Measuring the effect of input variables over their supports," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 17-27.
  46. Kleijnen, Jack P.C. & Mehdad, Ehsan, 2014. "Multivariate versus univariate Kriging metamodels for multi-response simulation models," European Journal of Operational Research, Elsevier, vol. 236(2), pages 573-582.
  47. Erickson, Collin B. & Ankenman, Bruce E. & Sanchez, Susan M., 2018. "Comparison of Gaussian process modeling software," European Journal of Operational Research, Elsevier, vol. 266(1), pages 179-192.
  48. Lauvernet, Claire & Helbert, Céline, 2020. "Metamodeling methods that incorporate qualitative variables for improved design of vegetative filter strips," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  49. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
  50. Giovanni Dosi & Marcelo C. Pereira & Andrea Roventini & Maria Enrica Virgillito, 2022. "A complexity view on the future of work. Meta-modelling exploration of the multi-sector K+S agent based model," LEM Papers Series 2022/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  51. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
  52. Hans Olav Vogt Myklebust & Jo Eidsvik & Iver Bakken Sperstad & Debarun Bhattacharjya, 2020. "Value of Information Analysis for Complex Simulator Models: Application to Wind Farm Maintenance," Decision Analysis, INFORMS, vol. 17(2), pages 134-153, June.
  53. Bachoc, François & Bevilacqua, Moreno & Velandia, Daira, 2019. "Composite likelihood estimation for a Gaussian process under fixed domain asymptotics," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
  54. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
  55. Mickaël Binois & David Ginsbourger & Olivier Roustant, 2020. "On the choice of the low-dimensional domain for global optimization via random embeddings," Journal of Global Optimization, Springer, vol. 76(1), pages 69-90, January.
  56. Victor Picheny & Mickael Binois & Abderrahmane Habbal, 2019. "A Bayesian optimization approach to find Nash equilibria," Journal of Global Optimization, Springer, vol. 73(1), pages 171-192, January.
  57. James Risk & Michael Ludkovski, 2015. "Statistical Emulators for Pricing and Hedging Longevity Risk Products," Papers 1508.00310, arXiv.org, revised Sep 2015.
  58. Lihua Sun & L. Jeff Hong & Zhaolin Hu, 2014. "Balancing Exploitation and Exploration in Discrete Optimization via Simulation Through a Gaussian Process-Based Search," Operations Research, INFORMS, vol. 62(6), pages 1416-1438, December.
  59. Michael Ludkovski, 2015. "Kriging Metamodels and Experimental Design for Bermudan Option Pricing," Papers 1509.02179, arXiv.org, revised Oct 2016.
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