報告題目：The use of machine learning techniques to estimate technical efficiency （用機器學習方法測算技術效率）
報告人：Juan Aparicio教授（Miguel Hernandez University of Elche (UMH), 西班牙）
北京時間：2023年6月22日（星期四）下午15:30
Zoom：835 4912 5671
密碼：230619
報告鏈接：https://us06web.zoom.us/j/83549125671?pwd="N1ZCWWFNZm5POXlybE5kZlhKMjMwdz09
報告摘要：
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.
報告人簡介：
Juan Aparicio是西班牙Miguel Hernandez University of Elche (UMH)統計、數學和信息技術系的教授，也是運籌學中心的負責人。他曾擔任桑坦德銀行效率和生產力主席的聯合主席（與Knox Lovell教授）。他的研究興趣包括與機器學習和數據科學相結合的效率與生產力分析。他與Springer出版社合作，獨立或共同編輯了幾本書，主要集中于使用數據包絡分析進行績效評估和基準測試；并在不同的國際期刊上發表了約150篇科學文章。這些期刊包括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。特別是，他最近發表了幾篇不同機器學習技術的改編文章，從方法論的角度估計生產函數和技術效率。此外，他還將新方法應用于教育、銀行等不同部門的真實數據庫。他曾在DEA International Conference in 2020等多個會議上擔任主旨發言人。最后，他目前是Omega，The International Journal of Management Science和Journal of Productivity Analysis的副主編。
（承辦：能源與環境政策研究中心、科研與學術交流中心）
