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Blake McShane
Blake McShane

MARKETING
Assistant Professor of Marketing

Print Overview
Blake McShane joined the marketing faculty at the Kellogg School of Management in 2010 as a Donald P. Jacobs Scholar. He has developed and applied statistical methodology to topics ranging from optimizing internet ad-serving algorithms to forecasting home runs in baseball. His specific research interests include Bayesian hierarchical modeling, statistical learning, and generalized Markov models. More generally, he seeks to develop statistical methods to accommodate the rich and varied data structures encountered in business problems and to use these methods to glean insight about individual behavior so as to test and supplement existing theories. Blake earned his PhD and MA in Statistics, MA and BA in Mathematics, and BS in Economics from the University of Pennsylvania .

Areas of Expertise
Bayesian Modeling
Data Analysis
Database Marketing
Marketing Research
Print Vita
Education
Ph.D., 2010, Statistics, Wharton School, University of Pennsylvania
M.A., 2010, Statistics, Wharton School, University of Pennsylvania
M.A., 2003, Mathematics, College of Arts and Sciences, University of Pennsylvania
B.A., 2003, Mathematics, College of Arts and Sciences, University of Pennsylvania
B.S., 2003, Economics, Wharton School, University of Pennsylvania

Academic Positions
Assistant Professor, Marketing, Kellogg School of Management, Northwestern University, 2011-present
Donald P. Jacobs Scholar, Marketing, Kellogg School of Management, Northwestern University, 2010-2011

Other Professional Experience

 
Print Research
Research Interests
Bayesian hierarchical modeling; statistical learning; generalized Markov models; probability models for marketing; developing new methodology for unique data structures with application to business problems

Articles
McShane, Blake, R.J. Galante, M.P. Biber, S. T. Jensen, A. J. Wyner and Pack, A.I.. Forthcoming. Assessing REM Sleep in Mice Using Video Data. Sleep.
Gal, David and Blake McShane. Forthcoming. Can Small Victories Help Win the War? Evidence from Consumer Debt Management. Journal of Marketing Research.
McShane, Blake, A. Braunstein, J. Piette and S. T. Jensen. 2011. A Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics. Journal of Quantitative Analysis in Sports. 7(4): 1-24.
McShane, Blake and A. J. Wyner. 2011. Rejoinder: A Statistical Analysis of Multiple Temperature Proxies: Are Reconstructions of Surface Temperatures Over the Last 1000 Years Reliable?. Annals of Applied Statistics. 5(1): 99-123.
McShane, Blake and A. J. Wyner. 2011. A Statistical Analysis of Multiple Temperature Proxies: Are Reconstructions of Surface Temperatures Over the Last 1000 Years Reliable?. Annals of Applied Statistics. 5(1): 5-44.
McShane, Blake, R.J. Galante, S. T. Jensen, Naidoo, N., Pack, A.I. and A. J. Wyner. 2010. Characterization of the Bout Durations of Sleep and Wakefulness: New Metrics for Summarizing Sleep. Journal of Neuroscience Methods. 193(2): 321-333.
Abstract
Piette, J., A. Braunstein, Blake McShane and S. T. Jensen. 2010. A Point-Mass Mixture Random Effects Model for Pitching Metrics. Journal of Quantitative Analysis in Sports. 6(3): Article 8.
Jensen, S. T., Blake McShane and A. J. Wyner. 2009. Rejoinder: Hierarchical Bayesian Modeling of Hitting Performance in Baseball. Bayesian Analysis. 4(4): 669-674.
Jensen, S. T., Blake McShane and A. J. Wyner. 2009. Hierarchical Bayesian Modeling of Hitting Performance in Baseball. Bayesian Analysis. 4(4): 631-652.
McShane, Blake, M. Adrian, Eric Bradlow and P. S. Fader. 2008. Count Models Based on Weibull Interarrival Times. Journal of Business and Economic Statistics. 26(3): 369-378.
Kiser, R., M. Asher and Blake McShane. 2008. Let’s Not Make a Deal: An Empirical Study of Decision Making in Unsuccessful Settlement Negotiations. Journal of Empirical Legal Studies. 5(3): 551-591.
Working Papers
McShane, Blake. More Evidence Contrary to the Statistical View of Boosting.
McShane, Blake, S. T. Jensen and A. J. Wyner. Integrating Machine Learning Methods with Hidden Markov Models: A New Approach to Categorical Time Series Analysis.
Wyner, A. J. and Blake McShane. Propensity Score Estimation with Machine Learning Methods: What are the Risks of Overfitting?.
Conference Proceedings
McShane, Blake. 2009. "Exploring a New Method for Classification with Local Time Dependence.".

 
Print Teaching
Teaching Interests
Marketing Research; Data Analysis; Computation Statistical Methods; Probability Models for Marketing
Full-Time / Part-Time MBA
Research Methods In Marketing (MKTG-450-0)

This course counts toward the following majors: Managerial Analytics, Marketing, Marketing Management

The broad objective of this course is to provide a fundamental understanding of marketing research methods employed by well-managed firms. The course focuses on integrating problem formulation, research design, questionnaire construction, sampling, data collection and data analysis to yield the most valuable information. The course also examines the proper use of statistical applications as well as qualitative methods, with an emphasis on the interpretation and use of results.