报告题目：The use of machine learning techniques to estimate technical efficiency （用机器学习方法测算技术效率）

报告人：Juan Aparicio教授（Miguel Hernandez University of Elche (UMH), 西班牙）

北京时间：2023年6月22日（星期四）下午15:30

Zoom：835 4912 5671

密码：230619

报告链接：https://us06web.zoom.us/j/83549125671?pwd="N1ZCWWFNZm5POXlybE5kZlhKMjMwdz09

报告摘要：

Free Disposal Hull (FDH) and Data Envelopment Analysis (DEA) present the typical characteristics of a data-driven approach with the specific objective of determining technical efficiency and production frontiers in Engineering and Microeconomics. However, by construction, the frontier estimators generated by FDH and DEA suffer from overfitting problems; something that contrasts with currently accepted models in machine learning. In this regard, FDH and DEA can be seen as statistical descriptive tools that make up of a more complex approach, where the aim is to avoid overfitting in order to conclude something about the underlying Data Generating Process that is behind the generation of the observations in a production process. In this presentation, we show how Efficiency Analysis Trees (EAT), which is based on the adaptation of regression trees in Machine Learning, can be a possible solution to overcome the overfitting problem associated with FDH and DEA. Additionally, we show other alternative adaptations of well-known machine learning techniques with the objective of determining technical efficiency of a set of homogeneous production units. Furthermore, we illustrate how these machine learning-based techniques may be used as complement to the standard non-parametric methods through some empirical applications.

报告人简介：

Juan Aparicio是西班牙Miguel Hernandez University of Elche (UMH)统计、数学和信息技术系的教授，也是运筹学中心的负责人。他曾担任桑坦德银行效率和生产力主席的联合主席（与Knox Lovell教授）。他的研究兴趣包括与机器学习和数据科学相结合的效率与生产力分析。他与Springer出版社合作，独立或共同编辑了几本书，主要集中于使用数据包络分析进行绩效评估和基准测试；并在不同的国际期刊上发表了约150篇科学文章。这些期刊包括European Journal of Operational Research，OMEGA，Annals of Operations Research，International Journal of Production Economics，Journal of Optimization Theory and Applications，Journal of Productivity Analysis，Operational Research，Socio-Economic Planning Sciences以及Computers and Operations Research and Computers and Industrial Engineering。特别是，他最近发表了几篇不同机器学习技术的改编文章，从方法论的角度估计生产函数和技术效率。此外，他还将新方法应用于教育、银行等不同部门的真实数据库。他曾在DEA International Conference in 2020等多个会议上担任主旨发言人。最后，他目前是Omega，The International Journal of Management Science和Journal of Productivity Analysis的副主编。

（承办：能源与环境政策研究中心、科研与学术交流中心）