Publications

Publications

Filter Results : (15) Arrow Down

Show Results For

Bayesian Estimation Remove Bayesian Estimation →

Page 1 of 15 Results

Are you looking for?

→ Search All HBS Web
  • March 2022
  • Article

Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless...  View Details
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
Related
  • Article

Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by...  View Details
Keywords: Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology
Related
  • August 2021
  • Article

Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use...  View Details
Keywords: Missing Data; Bayesian Statistics; Imputation; Categorical Data; Estimation
Related
  • March 2021
  • Article

Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment
By: Yang Xiang, Thomas Graeber, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the...  View Details
Keywords: Visual Perception; Bayesian Modeling; Perception; Judgments
Related
  • 2020
  • Working Paper

Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal...  View Details
Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation
Related
  • June 2018
  • Article

Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged
By: Clarence Lee, Elie Ofek and Thomas Steenburgh
We study how digital service firms can develop an active customer base, focusing on two questions. First, how does the way that customers use the service postadoption to meet their own needs (personal usage) and to interact with one another (social usage) vary across...  View Details
Keywords: Customer Engagement; Adoption Routes; Word-of-Mouth; Digital Marketing; Bayesian Estimation; Customers; Communication; Consumer Behavior; Marketing; Internet and the Web; Analytics and Data Science
Related
  • August 2017
  • Article

Incentives versus Reciprocity: Insights from a Field Experiment
By: Doug J. Chung and Das Narayandas
We conduct a field experiment in which we vary the sales force compensation scheme at an Asian enterprise that sells consumer durable goods. With variation generated by the experimental treatments, we model sales force performance to identify the effectiveness of...  View Details
Keywords: Sales Force Compensation; Field Experiment; Heterogeneity; Loss Aversion; Reciprocity; Salesforce Management; Compensation and Benefits
Related
  • Article

Learning from Potentially Biased Statistics: Household Inflation Perceptions and Expectations in Argentina
By: Alberto Cavallo, Guillermo Cruces and Ricardo Perez-Truglia
When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a...  View Details
Keywords: Inflation Expectations; Bayesian Estimation; Inflation and Deflation; Information; Household; Behavior; Argentina
Related
  • 2015
  • Working Paper

Incentives versus Reciprocity: Insights from a Field Experiment
By: Doug J. Chung and Das Narayandas
We conduct a field experiment in which we vary the sales force compensation scheme at an Asian enterprise that sells consumer durable goods. With variation generated by the experimental treatments, we model sales force performance to identify the effectiveness of...  View Details
Keywords: Sales Force Compensation; Field Experiment; Heterogeneity; Loss Aversion; Reciprocity; Motivation and Incentives; Salesforce Management; Compensation and Benefits
Related
  • 2013
  • Dissertation

Designing Freemium: A Model of Consumer Usage, Upgrade, and Referral Dynamics
By: Clarence Lee, Vineet Kumar and Sunil Gupta
Abstract. Over the past decade "freemium" (free + premium) has become the dominant business model among internet start-ups for its ability to acquire and monetize a large install-base with limited marketing resources. Freemium is a hybrid strategy where a firm offers...  View Details
Keywords: Discrete-Continuous Choice Dynamic Structural Models; Bayesian Estimation; Word-of-Mouth; Digital Services; Freemium; Entrepreneurship; Business Model; Motivation and Incentives; Marketing Strategy; Internet and the Web; Consumer Behavior; Marketing Reference Programs; Business Startups
Related
  • May 2011
  • Article

Overconfidence by Bayesian Rational Agents
By: Eric J. Van den Steen
This paper derives two mechanisms through which Bayesian-rational individuals with differing priors will tend to be relatively overconfident about their estimates and predictions, in the sense of overestimating the precision of these estimates. The intuition behind one...  View Details
Keywords: Decision Choices and Conditions; Forecasting and Prediction; Knowledge Acquisition; Risk Management; Prejudice and Bias
Related
  • 2010
  • Working Paper

Overconfidence by Bayesian Rational Agents
By: Eric Van den Steen
This paper derives two mechanisms through which Bayesian-rational individuals with differing priors will tend to be relatively overconfident about their estimates and predictions, in the sense of overestimating the precision of these estimates. The intuition behind one...  View Details
Keywords: Risk and Uncertainty; Measurement and Metrics; Game Theory; Forecasting and Prediction
Related
  • 2009
  • Working Paper

Do Friends Influence Purchases in a Social Network?
By: Raghuram Iyengar, Sangman Han and Sunil Gupta

Social networks, such as Facebook and Myspace have witnessed a rapid growth in their membership. Some of these businesses have tried an advertising-based model with very limited success. However, these businesses have not fully explored the power of their members to...  View Details

Keywords: Marketing; Network Effects; Sales; Power and Influence; Social and Collaborative Networks; Web Sites; South Korea
Related
  • November 2007
  • Background Note

Bayesian Estimation & Black-Litterman
By: Joshua D. Coval and Erik Stafford
Describes a practical method for asset allocation that is more robust to estimation errors than the traditional implementation of mean-variance optimization with sample means and covariances. The Bayesian inspired Black-Litterman model is described after introducing...  View Details
Keywords: Asset Management; Investment Portfolio; Mathematical Methods
Related
  • Forthcoming
  • Article

Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal...  View Details
Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation
Related

Are you looking for?

→ Search All HBS Web