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Show Results For
- All HBS Web
(1,776)
- People (9)
- News (315)
- Research (1,018)
- Events (12)
- Multimedia (10)
- Faculty Publications (837)
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- May 2013
- Teaching Note
Launching Krispy Natural: Cracking the Product Management Code (Brief Case)
By: Frank V. Cespedes and Heather Beckham
This case study concerns a review and interpretation of test market results for a new packaged good product. The purpose of the case is to provide students with practice and guidelines in the analysis of quantitative test market data while illustrating the roles of... View Details
- 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.
- 29 Jun 2015
- HBS Case
Consumer-centered Health Care Depends on Accessible Medical Records
Charlotte, which in 2014 owned and managed hospitals and acute care facilities in three states. In 2011, Carolinas launched Dickson Advanced Analytics, which incorporated complex clinical, financial, demographic, and claims data to develop View Details
- 16 Aug 2010
- Lessons from the Classroom
HBS Introduces Marketing Analysis Tools for Managers
The marketing analysis toolkits are a suite of analytical tools that managers can use to inform decision-making in marketing. Each toolkit includes a technical note that outlines the analysis technique, provides examples of how it is used... View Details
Keywords: by Sarah Jane Gilbert
- 2019
- Article
History, Micro Data, and Endogenous Growth
By: Ufuk Akcigit and Tom Nicholas
The study of economic growth is concerned with long-run changes, and therefore, historical data should be especially influential in informing the development of new theories. In this review, we draw on the recent literature to highlight areas in which study of history... View Details
Keywords: Economic Development; Growth; Innovation; Economic Growth; History; Analytics and Data Science; Innovation and Invention
Akcigit, Ufuk, and Tom Nicholas. "History, Micro Data, and Endogenous Growth." Annual Review of Economics 11 (2019): 615–633.
- Article
Making Private Data Accessible in an Opaque Industry: The Experience of the Private Capital Research Institute
By: Josh Lerner and Leslie Jeng
Private markets are becoming an increasingly important way of financing rapidly growing and mature firms, and private investors are reputed to have far-reaching economic impacts. These important markets, however, are uniquely difficult to study. This paper explores... View Details
Lerner, Josh, and Leslie Jeng. "Making Private Data Accessible in an Opaque Industry: The Experience of the Private Capital Research Institute." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 157–160.
- 2023
- Working Paper
Corporate Website-based Measures of Firms' Value Drivers
By: Wei Cai, Dennis Campbell and Patrick Ferguson
We develop and validate new text-based measures of firms’ financial and non-financial value drivers. Using the Wayback Machine to access public US firms’ archived websites from 1995-2020, we scrape text from corporate homepages. We use Kaplan and Norton’s (1992)... View Details
Cai, Wei, Dennis Campbell, and Patrick Ferguson. "Corporate Website-based Measures of Firms' Value Drivers." SSRN Working Paper Series, No. 4413808, April 2023.
- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details
Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
- May 2020
- Article
Scalable Holistic Linear Regression
By: Dimitris Bertsimas and Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
Bertsimas, Dimitris, and Michael Lingzhi Li. "Scalable Holistic Linear Regression." Operations Research Letters 48, no. 3 (May 2020): 203–208.
- August 2018 (Revised April 2019)
- Supplement
Chateau Winery (B): Supervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (B): Supervised Learning." Harvard Business School Supplement 119-024, August 2018. (Revised April 2019.)
- August 2018 (Revised April 2019)
- Case
Chateau Winery (A): Unsupervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers’ purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (A): Unsupervised Learning." Harvard Business School Case 119-023, August 2018. (Revised April 2019.)
- 2020
- Working Paper
An Empirical Guide to Investor-Level Private Equity Data from Preqin
By: Juliane Begenau, Claudia Robles-Garcia, Emil Siriwardane and Lulu Wang
This note provides guidance on the use of investor-level private equity data from Preqin for empirical research. Preqin primarily sources its cash flow data through Freedom of Information Act (FOIA) requests with U.S. public pensions. Our focus is on the components of... View Details
Keywords: Private Equity Returns; Prequin Data; Private Equity; Analytics and Data Science; Investment Return
Begenau, Juliane, Claudia Robles-Garcia, Emil Siriwardane, and Lulu Wang. "An Empirical Guide to Investor-Level Private Equity Data from Preqin." Working Paper, December 2020.
- 2019
- Article
Big Data
By: John A. Deighton
Big data is defined and distinguished from a mere moment in the “ancient quest to measure.” Specific discontinuities in the practice of information science are identified that, the paper argues, have large consequences for the social order. The infrastructure that runs... View Details
Keywords: Big Data; Digital Infrastructure; Privacy; Algorithm; Data Generators; Marketplace Icon; Analytics and Data Science; Infrastructure; Power and Influence; Society
Deighton, John A. "Big Data." Consumption, Markets & Culture 22, no. 1 (2019): 68–73.
- 2023
- Article
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in... View Details
Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
- March 2023
- Supplement
Allianz Türkiye (B): Adapting to a Changing World
By: John D. Macomber and Fares Khrais
Keywords: Insurance And Reinsurance; Natural Disasters; Turkey; Insurance; Climate Change; Analytics and Data Science; Insurance Industry; Financial Services Industry; Turkey
Macomber, John D., and Fares Khrais. "Allianz Türkiye (B): Adapting to a Changing World." Harvard Business School Supplement 223-076, March 2023.
- November–December 2015
- Article
Active Postmarketing Drug Surveillance for Multiple Adverse Events
By: Joel Goh, Margrét V. Bjarnadóttir, Mohsen Bayati and Stefanos A. Zenios
Postmarketing drug surveillance is the process of monitoring the adverse events of pharmaceutical or medical devices after they are approved by the appropriate regulatory authorities. Historically, such surveillance was based on voluntary reports by medical... View Details
Keywords: Drug Surveillance; Health Care; Stochastic Models; Queueing; Diffusion Approximation; Brownian Motion; Health Care and Treatment; Analytics and Data Science; Analysis
Goh, Joel, Margrét V. Bjarnadóttir, Mohsen Bayati, and Stefanos A. Zenios. "Active Postmarketing Drug Surveillance for Multiple Adverse Events." Operations Research 63, no. 6 (November–December 2015): 1528–1546. (Finalist, 2012 INFORMS Health Applications Society Pierskalla Award.)
- November 1998
- Teaching Note
Working with your "Shadow Partner" TN
By: Richard L. Nolan
Teaching Note for (9-399-051). View Details
- November 1990
- Case
Chemplan Corp.: Paint-Rite Division
By: Paul A. Vatter
An exercise with data that allows a discussion of regression analysis as a tool for forecasting and understanding structure. View Details
Vatter, Paul A. "Chemplan Corp.: Paint-Rite Division." Harvard Business School Case 191-090, November 1990.
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- December 1996 (Revised November 2006)
- Background Note
General Mills, Inc.: Appendix of Comparable Company Data
By: William J. Bruns Jr.
Financial ratios for comparable companies to be used in conjunction with an analysis of the General Mills Annual Report. View Details
Bruns, William J., Jr. "General Mills, Inc.: Appendix of Comparable Company Data." Harvard Business School Background Note 197-037, December 1996. (Revised November 2006.)