Overfitting Reduction in Convex Regression
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Rahul Mazumder & Arkopal Choudhury & Garud Iyengar & Bodhisattva Sen, 2019. "A Computational Framework for Multivariate Convex Regression and Its Variants," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 318-331, January.
- 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.
- Daisuke Yagi & Yining Chen & Andrew L. Johnson & Timo Kuosmanen, 2020.
"Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 43-54, January.
- Yagi, Daisuke & Chen, Yining & Johnson, Andrew L. & Kuosmanen, Timo, 2018. "Shape constrained kernel-weighted least squares: Estimating production functions for Chilean manufacturing industries," LSE Research Online Documents on Economics 86556, London School of Economics and Political Science, LSE Library.
- Lee, Chia-Yen & Johnson, Andrew L. & Moreno-Centeno, Erick & Kuosmanen, Timo, 2013. "A more efficient algorithm for Convex Nonparametric Least Squares," European Journal of Operational Research, Elsevier, vol. 227(2), pages 391-400.
- Kuosmanen, Timo & Zhou, Xun, 2021. "Shadow prices and marginal abatement costs: Convex quantile regression approach," European Journal of Operational Research, Elsevier, vol. 289(2), pages 666-675.
- Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
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.- Liao, Zhiqiang & Dai, Sheng & Kuosmanen, Timo, 2024. "Convex support vector regression," European Journal of Operational Research, Elsevier, vol. 313(3), pages 858-870.
- 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.
- Zhiqiang Liao, 2024. "Variable selection in convex nonparametric least squares via structured Lasso: An application to the Swedish electricity distribution networks," Papers 2409.01911, arXiv.org, revised Nov 2024.
- 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.
- 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.
- Rødseth, Kenneth Løvold, 2023. "Noise pollution of container handling: External and abatement costs and environmental efficiency," Transport Policy, Elsevier, vol. 134(C), pages 82-93.
- Cristina Polo & Julián Ramajo & Alejandro Ricci‐Risquete, 2021. "A stochastic semi‐non‐parametric analysis of regional efficiency in the European Union," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 7-24, February.
- Timo Kuosmanen & Sheng Dai, 2025.
"Modeling economies of scope in joint production: Convex regression of input distance function,"
Journal of Productivity Analysis, Springer, vol. 63(1), pages 69-86, February.
- Timo Kuosmanen & Sheng Dai, 2023. "Modeling economies of scope in joint production: Convex regression of input distance function," Papers 2311.11637, arXiv.org.
- 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.
- 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.
- 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.
- 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.
- 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.
- Chung, William & Chen, Yong-Tong, 2024. "A nonparametric least squares regression method for forecasting building energy performance," Applied Energy, Elsevier, vol. 376(PB).
- 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).
- Dai, Sheng & Kuosmanen, Timo & Zhou, Xun, 2023. "Non-crossing convex quantile regression," Economics Letters, Elsevier, vol. 233(C).
- 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.
- Ruitu Xu & Yifei Min & Tianhao Wang & Zhaoran Wang & Michael I. Jordan & Zhuoran Yang, 2023. "Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning," Papers 2303.04833, arXiv.org.
- Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022.
"Stochastic Frontier Analysis: Foundations and Advances I,"
Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370,
Springer.
- Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances II," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 9, pages 371-408, Springer.
- Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2017. "Stochastic Frontier Analysis: Foundations and Advances," Working Papers 2017-10, University of Miami, Department of Economics.
- Subal C. Kumbhakar & Christopher F. Parameter & Valentin Zelenyuk, 2018. "Stochastic Frontier Analysis: Foundations and Advances," CEPA Working Papers Series WP022018, School of Economics, University of Queensland, Australia.
- Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2024-05-20 (Econometrics)
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:arx:papers:2404.09528. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.