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- Faculty Publications (2)
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- All HBS Web (4)
- Faculty Publications (2)
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- 2019
- Working Paper
Large-Scale Demand Estimation with Search Data
By: Tomomichi Amano, Andrew Rhodes and Stephan Seiler
In many online markets, traditional methods of demand estimation are difficult to implement because assortments are very large and individual products are sold infrequently. At the same time, data on consumer search (i.e., browsing) behavior are often available and are... View Details
Amano, Tomomichi, Andrew Rhodes, and Stephan Seiler. "Large-Scale Demand Estimation with Search Data." Harvard Business School Working Paper, No. 19-022, September 2018. (Revised June 2019. Stanford University Research Paper, No. 18-36, 8-20 2018.)
- Article
Fast Generalized Subset Scan for Anomalous Pattern Detection
By: Edward McFowland III, Skyler Speakman and Daniel B. Neill
We propose Fast Generalized Subset Scan (FGSS), a new method for detecting anomalous patterns in general categorical data sets. We frame the pattern detection problem as a search over subsets of data records and attributes, maximizing a nonparametric scan statistic... View Details
Keywords: Pattern Detection; Anomaly Detection; Knowledge Discovery; Bayesian Networks; Scan Statistics; Analytics and Data Science
McFowland III, Edward, Skyler Speakman, and Daniel B. Neill. "Fast Generalized Subset Scan for Anomalous Pattern Detection." Art. 12. Journal of Machine Learning Research 14 (2013): 1533–1561.
- 24 Apr 2019
- HBS Seminar
Dimitris Papanikolaou, Kellogg School of Management, Northwestern University
- Web
Students on the Job Market - Doctoral
treated. Using this quasi-exogenous variation, monthly repurchase data and a staggered DiD design, my main tests find that the modernized rule reduces share repurchases amounts, primarily by reducing the number of firms repurchasing. In... View Details