報(bào)告題目：The use of machine learning techniques to estimate technical efficiency （用機(jī)器學(xué)習(xí)方法測(cè)算技術(shù)效率）
報(bào)告人：Juan Aparicio教授（Miguel Hernandez University of Elche (UMH), 西班牙）
北京時(shí)間：2023年6月22日（星期四）下午15:30
Zoom：835 4912 5671
密碼：230619
報(bào)告鏈接：https://us06web.zoom.us/j/83549125671?pwd="N1ZCWWFNZm5POXlybE5kZlhKMjMwdz09
報(bào)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(bào)告人簡(jiǎn)介：
Juan Aparicio是西班牙Miguel Hernandez University of Elche (UMH)統(tǒng)計(jì)、數(shù)學(xué)和信息技術(shù)系的教授，也是運(yùn)籌學(xué)中心的負(fù)責(zé)人。他曾擔(dān)任桑坦德銀行效率和生產(chǎn)力主席的聯(lián)合主席（與Knox Lovell教授）。他的研究興趣包括與機(jī)器學(xué)習(xí)和數(shù)據(jù)科學(xué)相結(jié)合的效率與生產(chǎn)力分析。他與Springer出版社合作，獨(dú)立或共同編輯了幾本書(shū)，主要集中于使用數(shù)據(jù)包絡(luò)分析進(jìn)行績(jī)效評(píng)估和基準(zhǔn)測(cè)試；并在不同的國(guó)際期刊上發(fā)表了約150篇科學(xué)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(fā)表了幾篇不同機(jī)器學(xué)習(xí)技術(shù)的改編文章，從方法論的角度估計(jì)生產(chǎn)函數(shù)和技術(shù)效率。此外，他還將新方法應(yīng)用于教育、銀行等不同部門(mén)的真實(shí)數(shù)據(jù)庫(kù)。他曾在DEA International Conference in 2020等多個(gè)會(huì)議上擔(dān)任主旨發(fā)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(huán)境政策研究中心、科研與學(xué)術(shù)交流中心）
