Google Scholar: 499 Citations; h-index: 7; i10-index: 6.
Articles
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Liao, Z., Dai, S.*, & Kuosmanen T. 2023. Convex Support Vector Regression. European Journal of Operational Research, In Press.
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Dai, S., Kuosmanen, T. & Zhou, X. 2023. Generalized quantile and expectile properties for shape constrained nonparametric estimation. European Journal of Operational Research, 310, 914-927.
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Kuosmanen, T., Tan, Y. & Dai, S. 2023. Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic. Health Care Management Science, In Press.
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Dai, S. 2023. Variable selection in convex quantile regression: L1-norm or L0-norm regularization? European Journal of Operational Research, 305, 338-355.
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Yi, J., Dai, S., Cheng, J., Wu, Q. & Liu, K. 2021. Production quota policy in China: Implications for sustainable supply capacity of critical minerals. Resources Policy, 72, 102046.
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Dai, S., Zhou, X. & Kuosmanen, T. 2020. Forward-looking assessment of the GHG abatement cost: Application to China. Energy Economics, 88, 104758.
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Kuosmanen, T., Zhou, X. & Dai, S. 2020. How much climate policy has cost for OECD countries? World Development, 125, 104681.
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Cheng, J., Yi, J., Dai, S. & Xiong, Y. 2019. Can low-carbon city construction facilitate green growth? Evidence from China’s pilot low-carbon city initiative. Journal of Cleaner Production, 231, 1158-1170. [ESI top 1%; Highly cited paper]
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Dai, Q., Ye, X., Wei, Y.D., Ning, Y. & Dai, S. 2018. Geography, ethnicity and regional inequality in Guangxi Zhuang autonomous region, China. Applied Spatial Analysis and Policy, 11, 557-580.
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Chen, J., Cheng, J. & Dai, S. 2017. Regional eco-innovation in China: An analysis of eco-innovation levels and influencing factors. Journal of Cleaner Production, 153, 1-14.
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Cheng, J., Dai, S.* & Ye, X. 2016. Spatiotemporal heterogeneity of industrial pollution in China. China Economic Review, 40, 179-191.
Preprints
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Dai, S., Kuosmanen, T., & Zhou, X. 2022. Non-crossing convex quantile regression. arXiv:2204.01371.
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Dai, S., Fang, Y.H., Lee, C.Y. & Kuosmanen, T. 2021. pyStoNED: A Python package for convex regression and frontier estimation. arXiv:2109.12962.
Technical reports
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Kuosmanen, T., Kuosmanen, N., & Dai, S. 2022. Kohtuullinen muuttuva kustannus sähkön jakeluverkkoyhtiöiden valvontamallissa: Ehdotus tehostamiskannustimen kehittämiseksi 6. ja 7. valvontajaksoilla vuosina 2024-2031. Energiavirasto, Finland.
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Dai, S., Kuosmanen, N., Kuusi, T., Kuosmanen, T. (Editor), Liesiö, J., & Maczulskij, T. 2022. Misallocation of labor and capital in Finland’s business sector. Prime Minister’s Office of Finland.
Thesis
- Dai, S. 2022. Essays on Convex Regression and Frontier Estimation. Aalto University publication series DOCTORAL THESES, 111/2022.