Open Access
| Issue |
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|
|
|---|---|---|
| Article Number | 02049 | |
| Number of page(s) | 7 | |
| Section | Mathematical Science and Application | |
| DOI | https://doi.org/10.1051/matecconf/202235502049 | |
| Published online | 12 January 2022 | |
- Affuso E. Spatial Auto regressive Stochastic Frontier Analysis: An Application to An Impact Evaluation Study[J].Working Paper, Departmentof Agricultural Economicsand Rural Sociology, Auburn University, 2010. [Google Scholar]
- Aigner D, Lovell K and Schmidt P. Formulationand Estimationof Stochastic Frontier Production Function Models[J]. Journal of Econometrics, 1977(6): 21–37. [CrossRef] [Google Scholar]
- Charnes A, Cooper W W and Rhddes E. Measuring the Efficiency of Decision-making Units[J]. European Journal of Operational Research, 1978(6):429–444. [CrossRef] [Google Scholar]
- Druska V, Horrace W C. Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming[J]. American Journal of Agricultural Economics, 2004(1):185–198. [CrossRef] [Google Scholar]
- Fare R, Grosskopf S. A Nonparametric Cost Approach to Scale Efficiency[J]. The Scandinavia Journal of Econoics, 1985(4):594–604. [CrossRef] [Google Scholar]
- Kapoor M, Kelejian H H, PruchaI R. Panel Data Model swith Spatially Correlated Error Components[J]. Journal of Econometrics, 2007(1):97–130. [CrossRef] [Google Scholar]
- Lan Haixia, Zhao Xueyan. Spatiotemporal evolution of regional innovation efficiency and influencing factors of innovation environment in China[J]. Economic Geography, 2020(2): 97–107. [Google Scholar]
- Ma Su, Gao liangmou, Zhao Guanghui. Research on enterprise life cycle division and efficiency based on bootstrap DEA model [J]. China Soft Science, 2019(11): 176–182. [Google Scholar]
- Ma Zhanxin, Zhao Jiafeng. Efficiency paradox and data short tail phenomenon of DEA method [J]. Systems Engineering-Theory & Practice, 2019(1): 200–214. [Google Scholar]
- Pitt M, and Lee L. The Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry[J]. Journal of Development Economics, 1981(9): 43–64. [CrossRef] [Google Scholar]
- Ray S C, Desli E. Productivity Growth, Technical Progress, and Eficiency Change in industrialized Comment[J]. Amerian Economic Review, 1997(5): 1033–1039. [Google Scholar]
- Tonini A, Pede V. A Generalized Maximum Entropy Stochastic Frontier Measuring Productivity Accounting for Spatial Dependency[J]. Entropy, 2011(11): 1916–1927. [CrossRef] [Google Scholar]
- Wang Chujun, Xu Zhi, Chen Liyu. Evaluation of transformation efficiency of scientific and technological achievements in China’s research universities based on Benchmarking Management-Application of network sequencing method[J]. Scientific Research Manageme, 2020(3): 183–193. [Google Scholar]
- Wang Pingping, Chen Bo. Research on technical efficiency and its influencing factors of civil military integration enterprises [J]. Management review, 2019(4): 70–82. [Google Scholar]
- Zhang Dayong, Zhang Zhiwei. Competition and efficiency: An Empirical Study Based on China’s regional commercial banks [J]. Journal of Financial Research, 2019(4): 111–129. [Google Scholar]
- Zhu Fangfang. Research on regional heterogeneity from the perspective of factor bias of innovation and technological progress-An Empirical Analysis Based on SFA and Guangdong data [J]. Journal of Applied Statistics and Management, 2019 (1): 16–27. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.