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Report NEP-BIG-2018-11-05
This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-BIG
The following items were announced in this report:
- Terry Gregory & A.M. Salomons & Ulrich Zierahn, 2018, "Racing With or Against the Machine?: Evidence from Europe," Working Papers, Utrecht School of Economics, number 18-07, Sep.
- John Voorheis, 2017, "Longitudinal Environmental Inequality and Environmental Gentrification: Who Gains From Cleaner Air?," CARRA Working Papers, Center for Economic Studies, U.S. Census Bureau, number 2017-04, May.
- John Voorheis, 2017, "Air Quality, Human Capital Formation and the Long-term Effects of Environmental Inequality at Birth," CARRA Working Papers, Center for Economic Studies, U.S. Census Bureau, number 2017-05, May.
- Goller, Daniel & Knaus, Michael C. & Lechner, Michael & Okasa, Gabriel, 2018, "Predicting Match Outcomes in Football by an Ordered Forest Estimator," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science, number 1811, Nov.
- Jie Ding & Vahid Tarokh & Yuhong Yang, 2018, "Model Selection Techniques -- An Overview," Papers, arXiv.org, number 1810.09583, Oct.
- Fuli Feng & Huimin Chen & Xiangnan He & Ji Ding & Maosong Sun & Tat-Seng Chua, 2018, "Enhancing Stock Movement Prediction with Adversarial Training," Papers, arXiv.org, number 1810.09936, Oct, revised Jun 2019.
- Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018, "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series, CESifo, number 7259.
- Marc Sabate Vidales & David Siska & Lukasz Szpruch, 2018, "Unbiased deep solvers for linear parametric PDEs," Papers, arXiv.org, number 1810.05094, Oct, revised Jan 2022.
- Chihli Hung, 2018, "Using Preference Vector Modeling to Polarity Shift for Improvement of Opinion Mining," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 6508391, Jul.
- Avraam Tsantekidis & Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018, "Using Deep Learning for price prediction by exploiting stationary limit order book features," Papers, arXiv.org, number 1810.09965, Oct.
- Shenhao Wang & Qingyi Wang & Nate Bailey & Jinhua Zhao, 2018, "Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective," Papers, arXiv.org, number 1810.10465, Oct, revised Sep 2019.
- Ehsan Hoseinzade & Saman Haratizadeh, 2018, "CNNPred: CNN-based stock market prediction using several data sources," Papers, arXiv.org, number 1810.08923, Oct.
- Youngjoon Lee & Soohyon Kim & Ki Young Park, 2018, "Deciphering Monetary Policy Committee Minutes with Text Mining Approach: A Case of South Korea," Working papers, Yonsei University, Yonsei Economics Research Institute, number 2018rwp-132, Oct.
- Kfir Eliaz & Ran Spiegler, 2018, "The Model Selection Curse," Papers, arXiv.org, number 1810.02888, Oct.