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Show Results For
-
All HBS Web
(834)
- News (77)
- Research (637)
- Events (11)
- Multimedia (4)
- Faculty Publications (632)
- November 1974 (Revised November 1977)
- Background Note
Developing Forecasts with the Aid of Regression Analysis
By: Paul A. Vatter
Vatter, Paul A. "Developing Forecasts with the Aid of Regression Analysis." Harvard Business School Background Note 175-105, November 1974. (Revised November 1977.)
- 2016
- Working Paper
Refugee Resettlement
By: David Delacretaz, Scott Duke Kominers and Alexander Teytelboym
Over 100,000 refugees are permanently resettled from refugee camps to hosting
countries every year. Nevertheless, refugee resettlement processes in most countries
are ad hoc, accounting for neither the priorities of hosting communities nor the preferences of refugees...
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Delacretaz, David, Scott Duke Kominers, and Alexander Teytelboym. "Refugee Resettlement." Working Paper, November 2016.
- October 2012 (Revised October 2016)
- Case
Predilytics
By: Robert F. Higgins and Annelena Lobb
The management team at Predilytics, a healthcare analytics firm, must decide whether to accept a Series A venture capital financing deal. The company provided analytic services to healthcare plans, typically Medicare Advantage plans, in efforts to draw conclusions from...
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Keywords:
Analytics;
Healthcare;
Medicare;
Health Care and Treatment;
Mathematical Methods;
Health Industry;
New England;
United States
Higgins, Robert F., and Annelena Lobb. "Predilytics." Harvard Business School Case 813-023, October 2012. (Revised October 2016.)
- 2008
- Chapter
Moving to a New Global Competitiveness Index
By: Michael E. Porter, Mercedes Delgado-Garcia, Christian H.M. Ketels and Scott Stern
Porter, Michael E., Mercedes Delgado-Garcia, Christian H.M. Ketels, and Scott Stern. "Moving to a New Global Competitiveness Index." Chap. 1.2 in Global Competitiveness Report 2008/2009, edited by Michael E. Porter and Klaus Schwab, 43–63. Geneva: World Economic Forum, 2008.
- August 2006
- Article
Confidence Intervals for Probabilities of Default
By: Samuel G. Hanson and Til Schuermann
In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric bootstrap methods. We do so for two different PD estimation...
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Hanson, Samuel G., and Til Schuermann. "Confidence Intervals for Probabilities of Default." Journal of Banking & Finance 30, no. 8 (August 2006).
- December 1981 (Revised September 1986)
- Background Note
Research Methods in Marketing: Survey Research
By: Robert J. Dolan
Presents basic issues in survey research, covering both measurement and sampling error. The intention is to consider each element of the survey process: problem statement, questionnaire design, sampling, and data analysis.
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Dolan, Robert J. "Research Methods in Marketing: Survey Research." Harvard Business School Background Note 582-055, December 1981. (Revised September 1986.)
- 1969
- Other Unpublished Work
An Empirical Investigation of the Samuelson Rational Warrant Pricing Theory
By: Robert C. Merton
- Profile
Tessa Vacher-Desvernais
analytics, but truly appreciate aesthetics. I’m very conscious of my inner tension between analytical and creative thinking.” Following her mathematical and sciences baccalaureate, she pursued liberal arts at an all-girl military boarding...
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- 01 Mar 2014
- News
Chances Are
consultant. He fears a coming mathematical feudalism. "We are left with a kind of elite that knows how to think about this stuff and a large mass of people who are really very vulnerable." The Book of Odds, then, is Shapiro's attempt to...
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- 01 Jun 1997
- News
Jennifer L. Scott
year the show is reinvented," she explains. "It's a lot like a startup." Scott has always sought out new challenges and ventures. After graduating from Georgetown University's School of Foreign Service, she taught mathematics and business...
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Keywords:
Susan Young
- 26 May 2022
- News
Bidding Up
blah, blah. Talking about a case and the antipathy towards theory, the lack of a foundation in disciplines like economics and mathematics and so on, I thought was appalling. Okay. So on one WAC, you know, Written Analysis of Case. Okay....
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- 19 Oct 2017
- Working Paper Summaries
Games of Threats
- Forthcoming
- Article
Preference Externality Estimators: A Comparison of Border Approaches and IVs
By: Xi Ling, Wesley R. Hartmann and Tomomichi Amano
This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two dimensions in...
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Ling, Xi, Wesley R. Hartmann, and Tomomichi Amano. "Preference Externality Estimators: A Comparison of Border Approaches and IVs." Management Science (forthcoming). (Pre-published online January 23, 2024.)
- February 2005
- Article
An Econometric Analysis of Inventory Turnover Performance in Retail Services
By: Vishal Gaur, Marshall L. Fisher and Ananth Raman
Gaur, Vishal, Marshall L. Fisher, and Ananth Raman. "An Econometric Analysis of Inventory Turnover Performance in Retail Services." Management Science 51, no. 2 (February 2005): 181–194.
- 22 Sep 2015
- News
Turning Troubled Schools into High-Achievers
of stories of success. If I think about one of our turnaround schools, before we intervened at that school, only about 12 or 13 percent of students were demonstrating grade-level proficiency in mathematics and in reading. We were asked to...
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- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available...
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Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
- 2024
- Working Paper
Bootstrap Diagnostics for Irregular Estimators
By: Isaiah Andrews and Jesse M. Shapiro
Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience. When such approximations are unreliable, they can lead the audience to make misguided decisions. We...
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Andrews, Isaiah, and Jesse M. Shapiro. "Bootstrap Diagnostics for Irregular Estimators." NBER Working Paper Series, No. 32038, January 2024.
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first...
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Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models...
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Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving...
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Keywords:
Autoencoder Networks;
Pattern Detection;
Subset Scanning;
Computer Vision;
Statistical Methods And Machine Learning;
Machine Learning;
Deep Learning;
Data Mining;
Big Data;
Large-scale Systems;
Mathematical Methods;
Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).