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Analysis of regression in game theory approach

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Cited by:

  1. Stan Lipovetsky, 2021. "Predictor Analysis in Group Decision Making," Stats, MDPI, vol. 4(1), pages 1-14, February.
  2. James V. Hansen, 2021. "Coalition Feature Interpretation and Attribution in Algorithmic Trading Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 849-866, October.
  3. Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  4. Emrah Arbak, 2017. "Identifying the provisioning policies of Belgian banks," Working Paper Research 326, National Bank of Belgium.
  5. Miklós Pintér, 2011. "Regression games," Annals of Operations Research, Springer, vol. 186(1), pages 263-274, June.
  6. Liu, Yuqi & Wang, Aiwen & Li, Bo & Šimůnek, Jirka & Liao, Renkuan, 2024. "Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize," Agricultural Water Management, Elsevier, vol. 303(C).
  7. Yung-Hsiang Ying & Wen-Li Lee & Ying-Chen Chi & Mei-Jung Chen & Koyin Chang, 2022. "Demographics, Socioeconomic Context, and the Spread of Infectious Disease: The Case of COVID-19," IJERPH, MDPI, vol. 19(4), pages 1-24, February.
  8. Jacobs, Martin & Requate, Till, 2016. "Demand rationing in Bertrand-Edgeworth markets with fixed capacities: An experiment," Economics Working Papers 2016-03, Christian-Albrechts-University of Kiel, Department of Economics.
  9. Dmitry Sharapov & Paul Kattuman & Diego Rodriguez & F. Javier Velazquez, 2021. "Using the SHAPLEY value approach to variance decomposition in strategy research: Diversification, internationalization, and corporate group effects on affiliate profitability," Strategic Management Journal, Wiley Blackwell, vol. 42(3), pages 608-623, March.
  10. Stan Lipovetsky, 2023. "Quantum-like Data Modeling in Applied Sciences: Review," Stats, MDPI, vol. 6(1), pages 1-9, February.
  11. Stan Lipovetsky & W. Michael Conklin, 2015. "Predictor relative importance and matching regression parameters," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 1017-1031, May.
  12. Viglia, Giampaolo & Abrate, Graziano, 2017. "When distinction does not pay off - Investigating the determinants of European agritourism prices," Journal of Business Research, Elsevier, vol. 80(C), pages 45-52.
  13. Haim Shalit, 2020. "The Shapley value of regression portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 506-512, October.
  14. Gabriel Ferrettini & Elodie Escriva & Julien Aligon & Jean-Baptiste Excoffier & Chantal Soulé-Dupuy, 2022. "Coalitional Strategies for Efficient Individual Prediction Explanation," Information Systems Frontiers, Springer, vol. 24(1), pages 49-75, February.
  15. Xingwei Hu, 2018. "A Theory of Dichotomous Valuation with Applications to Variable Selection," Papers 1808.00131, arXiv.org, revised Mar 2020.
  16. Ruiqiao Bai & Jacqueline C. K. Lam & Victor O. K. Li, 2023. "What dictates income in New York City? SHAP analysis of income estimation based on Socio-economic and Spatial Information Gaussian Processes (SSIG)," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
  17. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
  18. Wang, Jianxin, 2022. "Market distraction and near-zero daily volatility persistence," International Review of Financial Analysis, Elsevier, vol. 80(C).
  19. repec:jss:jstsof:33:i10 is not listed on IDEAS
  20. Beltrán, Sergio & Castro, Alain & Irizar, Ion & Naveran, Gorka & Yeregui, Imanol, 2022. "Framework for collaborative intelligence in forecasting day-ahead electricity price," Applied Energy, Elsevier, vol. 306(PA).
  21. Jeffrey H. Bergstrand & Jordi Paniagua, 2024. "Do Deep Trade Agreements’ Provisions Actually Increase – or Decrease – Trade and/or FDI?," CESifo Working Paper Series 11526, CESifo.
  22. Borgonovo, Emanuele & Plischke, Elmar & Rabitti, Giovanni, 2024. "The many Shapley values for explainable artificial intelligence: A sensitivity analysis perspective," European Journal of Operational Research, Elsevier, vol. 318(3), pages 911-926.
  23. Riccardo Colini-Baldeschi & Marco Scarsini & Stefano Vaccari, 2018. "Variance Allocation and Shapley Value," Methodology and Computing in Applied Probability, Springer, vol. 20(3), pages 919-933, September.
  24. Retzer, J.J. & Soofi, E.S. & Soyer, R., 2009. "Information importance of predictors: Concept, measures, Bayesian inference, and applications," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2363-2377, April.
  25. Lipovetsky, Stan & Conklin, Michael, 2014. "Finding items cannibalization and synergy by BWS data," Journal of choice modelling, Elsevier, vol. 12(C), pages 1-9.
  26. Wen Luo & Razia Azen, 2013. "Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 3-31, February.
  27. Emrah Arbak, 2017. "Identifying the provisioning policies of Belgian banks," Working Paper Research 326, National Bank of Belgium.
  28. Gi-Wook Cha & Choon-Wook Park & Young-Chan Kim & Hyeun Jun Moon, 2023. "Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks," Sustainability, MDPI, vol. 15(23), pages 1-22, November.
  29. Arevik Mkrtchyan, 2015. "Determining the Common External Tariff in a Customs Union: Evidence from the Eurasian Customs Union," BEROC Working Paper Series 27, Belarusian Economic Research and Outreach Center (BEROC).
  30. Xingwei Hu, 2020. "A theory of dichotomous valuation with applications to variable selection," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1075-1099, November.
  31. Eranga M. Wimalasiri & Ebrahim Jahanshiri & Tengku Adhwa Syaherah Tengku Mohd Suhairi & Hasika Udayangani & Ranjith B. Mapa & Asha S. Karunaratne & Lal P. Vidhanarachchi & Sayed N. Azam-Ali, 2020. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
  32. Serafeim Moustakidis & Spyridon Plakias & Christos Kokkotis & Themistoklis Tsatalas & Dimitrios Tsaopoulos, 2023. "Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics," Future Internet, MDPI, vol. 15(5), pages 1-18, May.
  33. Elena Pokryshevskaya & Evgeny Antipov, 2013. "Importance-performance analysis for internet stores: a system based on publicly available panel data," HSE Working papers WP BRP 08/MAN/2013, National Research University Higher School of Economics.
  34. Filotto, Umberto & Caratelli, Massimo & Fornezza, Fabrizio, 2021. "Shaping the digital transformation of the retail banking industry. Empirical evidence from Italy," European Management Journal, Elsevier, vol. 39(3), pages 366-375.
  35. Pera, Rebecca & Viglia, Giampaolo & Furlan, Roberto, 2016. "Who Am I? How Compelling Self-storytelling Builds Digital Personal Reputation," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 44-55.
  36. Robiul Islam & Andrey V. Andreev & Natalia N. Shusharina & Alexander E. Hramov, 2022. "Explainable Machine Learning Methods for Classification of Brain States during Visual Perception," Mathematics, MDPI, vol. 10(15), pages 1-25, August.
  37. Buckmann, Marcus & Joseph, Andreas, 2022. "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers 984, Bank of England.
  38. Pelin Ayranci & Phung Lai & Nhathai Phan & Han Hu & Alexander Kolinowski & David Newman & Deijing Dou, 2022. "OnML: an ontology-based approach for interpretable machine learning," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 770-793, August.
  39. Aas Kjersti & Nagler Thomas & Jullum Martin & Løland Anders, 2021. "Explaining predictive models using Shapley values and non-parametric vine copulas," Dependence Modeling, De Gruyter, vol. 9(1), pages 62-81, January.
  40. Khoa Tran & Hai-Canh Vu & Lam Pham & Nassim Boudaoud & Ho-Si-Hung Nguyen, 2024. "Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines," Mathematics, MDPI, vol. 12(10), pages 1-25, May.
  41. 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).
  42. Liu, Jiefeng & Zhang, Zhenhao & Fan, Xianhao & Zhang, Yiyi & Wang, Jiaqi & Zhou, Ke & Liang, Shuo & Yu, Xiaoyong & Zhang, Wei, 2022. "Power system load forecasting using mobility optimization and multi-task learning in COVID-19," Applied Energy, Elsevier, vol. 310(C).
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