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Show Results For
- All HBS Web
(838)
- News (79)
- Research (640)
- Events (14)
- Multimedia (4)
- Faculty Publications (632)
- 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. View Details
Dolan, Robert J. "Research Methods in Marketing: Survey Research." Harvard Business School Background Note 582-055, December 1981. (Revised September 1986.)
- 01 Oct 1999
- News
Eight Among Many: Charles W. ("Bill") Cassell
example helped Cassell consider becoming a teacher himself. "It was fascinating to observe how Steve's channeling of the children's excitement about their ideas led students to 'discover' things such as drawing to scale and abstract View Details
Keywords: Eileen McCluskey
- 01 Mar 2015
- News
The Next Big Swing
in 1987, the game puts players in the manager’s seat, letting them make tactical decisions against a mathematical model that determines outcomes based on everything from how a batter has historically fared against a certain pitcher to the... View Details
- June 2012
- Response
Solution to Exchanges 10.2 Puzzle: Borrowing in the Limit as Our Nerdiness Goes to Infinity
By: Ran I. Shorrer
This is a solution to the editor's puzzle from issue 10.2 of SIGecom Exchanges [Reeves 2011]. The puzzle asks to determine a point in time such that a lump sum payment of $S will be equivalent to a continuous stream of infinitesimal payments totaling $S, spread evenly... View Details
Shorrer, Ran I. "Solution to Exchanges 10.2 Puzzle: Borrowing in the Limit as Our Nerdiness Goes to Infinity." ACM SIGecom Exchanges 11, no. 1 (June 2012): 39–41.
- September 2010 (Revised January 2011)
- Background Note
Using Regression Analysis to Estimate Time Equations
This note presents a simple way to estimate time equations using regression analysis in Excel. The note quickly outlines regression analysis, then presents a real-life case example from the natural gas industry that students can use to gain experience developing and... View Details
Martinez-Jerez, Francisco de Asis, and Ariel Andres Blumenkranc. "Using Regression Analysis to Estimate Time Equations." Harvard Business School Background Note 111-001, September 2010. (Revised January 2011.)
- June 2007
- Article
Which Levers Boost ROI?
By: Margeaux Cvar and John A. Quelch
The article refers to ROI, or return on investment, and focuses on a rational strategy for financial markets that uses outside industry comparisons. The first step is to identify parallel businesses that have similar characteristics such as growth, capital, and market... View Details
Cvar, Margeaux, and John A. Quelch. "Which Levers Boost ROI?" Harvard Business Review 85, no. 6 (June 2007): 21–24.
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details
Neel, Seth. "PRIMO: Private Regression in Multiple Outcomes." Working Paper, March 2023.
- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- March 2020
- Article
Diagnosing Missing Always at Random in Multivariate Data
By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin
Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first... View Details
Keywords: Missing Data; Diagnostic Tools; Sensitivity Analysis; Hypothesis Testing; Missing At Random; Row Exchangeability; Analytics and Data Science; Mathematical Methods
Bojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing Missing Always at Random in Multivariate Data." Biometrika 107, no. 1 (March 2020): 246–253.
- June 2000
- Article
On the Regulatory Application of Efficiency Measures
The last decade has witnessed a change to more powerful incentive schemes and the adoption by a large number of regulators of some form of price cap regimes. The efficiency frontiers literature tackles the problem of measuring the X factor in a price cap regime... View Details
Ruzzier, Christian Alejandro. "On the Regulatory Application of Efficiency Measures." Utilities Policy 9, no. 2 (June 2000): 81–92. (with M. Rossi.)
- August 2001 (Revised July 2008)
- Technical Note
A Technical Note and Discussion on Real Estate Valuation (IBET): Back of the Envelope (BOE) on Bonhomme Place: A Case within a Case
By: Arthur I Segel
Discusses real estate valuation. Reviews "back of the envelope" valuation; real estate appraisal methods, including the income method; market comparables and replacement costs; and more complex computer modeling. Also discusses other variables that could influence... View Details
Segel, Arthur I. "A Technical Note and Discussion on Real Estate Valuation (IBET): Back of the Envelope (BOE) on Bonhomme Place: A Case within a Case." Harvard Business School Technical Note 802-025, August 2001. (Revised July 2008.)
- 01 Dec 2017
- News
Ink: Alumni Book Recommendations
understanding of human psychology, without the acceptance that we are all crazy, irrational, impulsive, emotionally driven animals, all the raw intelligence and mathematical logic in the world is little help in the fraught, shifting... View Details
- 2025
- Working Paper
Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure
By: Jason Milionis, Jens Ernstberger, Joseph Bonneau, Scott Duke Kominers and Tim Roughgarden
We introduce the first formal model capturing the elicitation of unverifiable information from a party (the "source") with implicit signals derived by other players (the "observers"). Our model is motivated in part by applications in decentralized physical... View Details
Milionis, Jason, Jens Ernstberger, Joseph Bonneau, Scott Duke Kominers, and Tim Roughgarden. "Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure." Working Paper, March 2025.
- Forthcoming
- Article
Slowly Varying Regression Under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression Under Sparsity." Operations Research (forthcoming). (Pre-published online March 27, 2024.)
- 2024
- Working Paper
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python
By: Melissa Ouellet and Michael W. Toffel
This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in... View Details
Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods
Ouellet, Melissa, and Michael W. Toffel. "Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python." Harvard Business School Working Paper, No. 25-010, August 2024.
- 2021
- Working Paper
Impact Investing: A Theory of Financing Social Enterprises
By: Benjamin N. Roth
I present a model of financing social enterprises to delineate the role of impact investors relative to “pure” philanthropists. I characterize the optimal scale and structure of a social enterprise when financed by grants, and when financed by investments. Impact... View Details
Roth, Benjamin N. "Impact Investing: A Theory of Financing Social Enterprises." Harvard Business School Working Paper, No. 20-078, February 2020. (Revised June 2021.)
- Article
Quantile Evaluation, Sensitivity to Bracketing, and Sharing Business Payoffs
By: Y. Grushka-Cockayne, K. C. Lichtendahl, V.R.R. Jose and R.L. Winkler
From forecasting competitions to conditional value-at-risk requirements, the use of multiple quantile assessments is growing in practice. To evaluate them, we use a rule from the general class of proper scoring rules for a forecaster’s multiple quantiles of a single... View Details
Grushka-Cockayne, Y., K. C. Lichtendahl, V.R.R. Jose, and R.L. Winkler. "Quantile Evaluation, Sensitivity to Bracketing, and Sharing Business Payoffs." Operations Research 65, no. 3 (May–June 2017): 712–728.
- January 2000
- Article
Maxmin Expected Utility through Statewise Combinations
By: Ramon Casadesus-Masanell, Peter Klibanoff and Emre Ozdenoren
This paper provides an axiomatic foundation for a maxmin expected utility over a set of priors (MMEU) decision rule in an environment where the elements of choice are Savage acts. The key axioms are stated using statewise combinations as in Gul (1992). View Details
Casadesus-Masanell, Ramon, Peter Klibanoff, and Emre Ozdenoren. "Maxmin Expected Utility through Statewise Combinations." Economics Letters 66, no. 1 (January 2000): 49–54.
- March 2016 (Revised January 2020)
- Teaching Note
Behavioural Insights Team (A) and (B)
By: Michael Luca and Patrick Rooney
The Behavioural Insights Team case introduces students to the concept of choice architecture and the value of experimental methods (sometimes called A/B testing) within organizational contexts. The exercise provides an opportunity for students to apply these principles... View Details