A more efficient algorithm for Convex Nonparametric Least Squares

Citations

Cited by:

1. Jose M. Cordero & Cristina Polo & Daniel Santín, 2020. "Assessment of new methods for incorporating contextual variables into efficiency measures: a Monte Carlo simulation," Operational Research, Springer, vol. 20(4), pages 2245-2265, December.
2. Lee, Chia-Yen & Wang, Ke, 2019. "Nash marginal abatement cost estimation of air pollutant emissions using the stochastic semi-nonparametric frontier," European Journal of Operational Research, Elsevier, vol. 273(1), pages 390-400.
3. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2017. "Specification of merger gains in the Norwegian electricity distribution industry," Energy Policy, Elsevier, vol. 102(C), pages 96-107.
4. José Luis Preciado Arreola & Daisuke Yagi & Andrew L. Johnson, 2020. "Insights from machine learning for evaluating production function estimators on manufacturing survey data," Journal of Productivity Analysis, Springer, vol. 53(2), pages 181-225, April.
5. Pang Du & Christopher F. Parmeter & Jeffrey S. Racine, 2012. "Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints," Department of Economics Working Papers 2012-08, McMaster University.
6. K. Hervé Dakpo & Yann Desjeux & Laure Latruffe, 2023. "Cost of abating excess nitrogen on wheat plots in France: An assessment with multi‐technology modelling," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(3), pages 800-815, September.
   • H Dakpo & Yann Desjeux & Laure Latruffe, 2023. "Cost of abating excess nitrogen on wheat plots in France: An assessment with multi‐technology modelling," Post-Print hal-04081036, HAL.
7. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
8. Mekaroonreung, Maethee & Johnson, Andrew L., 2014. "A nonparametric method to estimate a technical change effect on marginal abatement costs of U.S. coal power plants," Energy Economics, Elsevier, vol. 46(C), pages 45-55.
9. Hishinuma, Kazuhiro & Iiduka, Hideaki, 2020. "Fixed point quasiconvex subgradient method," European Journal of Operational Research, Elsevier, vol. 282(2), pages 428-437.
10. Tsionas, Mike G., 2023. "Clustering and meta-envelopment in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 304(2), pages 763-778.
11. Seifert, Stefan, 2015. "Productivity Growth and its Sources - A StoNED Metafrontier Analyis of the German Electricity Generating Sector," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112975, Verein für Socialpolitik / German Economic Association.
12. Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
13. Chia-Yen Lee, 2017. "Directional marginal productivity: a foundation of meta-data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 544-555, May.
14. Tsionas, Mike G., 2023. "Performance estimation when the distribution of inefficiency is unknown," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1212-1222.
15. Stefan Seifert, 2015. "Measuring Productivity When Technologies Are Heterogeneous: A Semi-Parametric Approach for Electricity Generation," Discussion Papers of DIW Berlin 1526, DIW Berlin, German Institute for Economic Research.
16. Tsionas, Mike G., 2023. "Joint production in stochastic non-parametric envelopment of data with firm-specific directions," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1336-1347.
17. Tsionas, Mike G. & Izzeldin, Marwan, 2018. "Smooth approximations to monotone concave functions in production analysis: An alternative to nonparametric concave least squares," European Journal of Operational Research, Elsevier, vol. 271(3), pages 797-807.
18. Lee, Chia-Yen & Cai, Jia-Ying, 2020. "LASSO variable selection in data envelopment analysis with small datasets," Omega, Elsevier, vol. 91(C).
19. Preciado Arreola, José Luis & Johnson, Andrew L. & Chen, Xun C. & Morita, Hiroshi, 2020. "Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method," European Journal of Operational Research, Elsevier, vol. 287(2), pages 699-711.
20. Dai, Sheng, 2023. "Variable selection in convex quantile regression: L1-norm or L0-norm regularization?," European Journal of Operational Research, Elsevier, vol. 305(1), pages 338-355.
21. Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Generalized quantile and expectile properties for shape constrained nonparametric estimation," European Journal of Operational Research, Elsevier, vol. 310(2), pages 914-927.
22. Lee, Chia-Yen, 2016. "Most productive scale size versus demand fulfillment: A solution to the capacity dilemma," European Journal of Operational Research, Elsevier, vol. 248(3), pages 954-962.
23. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
24. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
25. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2016. "Specification of merger gains in the Norwegian electricity distribution industry," Discussion Papers 2016/7, Norwegian School of Economics, Department of Business and Management Science.
26. Keshvari, Abolfazl, 2017. "A penalized method for multivariate concave least squares with application to productivity analysis," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1016-1029.