Random forest based quantile-oriented sensitivity analysis indices estimation
Author
Abstract
Suggested Citation
DOI: 10.1007/s00180-023-01450-5
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Kucherenko, Sergei & Song, Shufang & Wang, Lu, 2019. "Quantile based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 35-48.
- Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
- Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
- Jean-Claude Fort & Thierry Klein & Nabil Rachdi, 2016. "New sensitivity analysis subordinated to a contrast," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(15), pages 4349-4364, August.
- Maume-Deschamps, Véronique & Niang, Ibrahima, 2018. "Estimation of quantile oriented sensitivity indices," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 122-127.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Marrel, Amandine & Chabridon, Vincent, 2021. "Statistical developments for target and conditional sensitivity analysis: Application on safety studies for nuclear reactor," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
- Zdeněk Kala, 2021. "New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability," Mathematics, MDPI, vol. 9(19), pages 1-20, September.
- Fissler, Tobias & Pesenti, Silvana M., 2023. "Sensitivity measures based on scoring functions," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1408-1423.
- Tobias Fissler & Silvana M. Pesenti, 2022. "Sensitivity Measures Based on Scoring Functions," Papers 2203.00460, arXiv.org, revised Jul 2022.
- Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
- Song, Shufang & Bai, Zhiwei & Kucherenko, Sergei & Wang, Lu & Yang, Caiqiong, 2021. "Quantile sensitivity measures based on subset simulation importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
- Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022. "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers 2207.04368, arXiv.org, revised Oct 2022.
- Joshua Rosaler & Luca Candelori & Vahagn Kirakosyan & Kharen Musaelian & Ryan Samson & Martin T. Wells & Dhagash Mehta & Stefano Pasquali, 2025. "Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning," Papers 2502.01495, arXiv.org.
- David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
- Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Mendez, Guillermo & Lohr, Sharon, 2011. "Estimating residual variance in random forest regression," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2937-2950, November.
- Pilowsky, Julia A. & Manica, Andrea & Brown, Stuart & Rahbek, Carsten & Fordham, Damien A., 2022. "Simulations of human migration into North America are more sensitive to demography than choice of palaeoclimate model," Ecological Modelling, Elsevier, vol. 473(C).
- Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
- Yi Fu & Shuai Cao & Tao Pang, 2020. "A Sustainable Quantitative Stock Selection Strategy Based on Dynamic Factor Adjustment," Sustainability, MDPI, vol. 12(10), pages 1-12, May.
- José María Sarabia & Faustino Prieto & Vanesa Jordá & Stefan Sperlich, 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis," Risks, MDPI, vol. 8(2), pages 1-14, April.
- Biau, Gérard & Devroye, Luc, 2010. "On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2499-2518, November.
- Olivier BIAU & Angela D´ELIA, 2010. "Euro Area GDP Forecast Using Large Survey Dataset - A Random Forest Approach," EcoMod2010 259600029, EcoMod.
- Lei, Hongxuan & Liu, Pan & Cheng, Qian & Xu, Huan & Liu, Weibo & Zheng, Yalian & Chen, Xiangding & Zhou, Yong, 2024. "Frequency, duration, severity of energy drought and its propagation in hydro-wind-photovoltaic complementary systems," Renewable Energy, Elsevier, vol. 230(C).
- Cleridy E. Lennert‐Cody & Richard A. Berk, 2007. "Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 671-689, July.
- Zdeněk Kala, 2020. "Sensitivity Analysis in Probabilistic Structural Design: A Comparison of Selected Techniques," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
More about this item
Keywords
Quantile-oriented sensitivity analysis; Random forest; Cross validation; Out-of-bag samples;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:39:y:2024:i:4:d:10.1007_s00180-023-01450-5. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.