Michael Parzen

Associate Professor of the Practice, Administrative Sciences

Drawing on three decades of experience in applied business analytics, data analysis, and statistical modeling, Dr. Parzen’s research focuses on developing innovative statistical methods and applying them to real-world business challenges. His commitment to integrating rigorous analytical techniques with practical solutions equips students with the skills necessary to leverage data for strategic advantage.

Prior to joining Boston University’s Metropolitan College, Dr. Parzen held full-time faculty positions at Harvard Business School, Harvard College, Emory University’s Goizueta Business School, and the University of Chicago’s Booth School of Business. He has also played key roles in curriculum design and program leadership, earning recognition for teaching excellence across institutions.

Dr. Parzen has published extensively in the fields of applied statistics, computational statistics, and data-driven decision-making, with his work appearing in leading journals such as Biometrics, Journal of Computational and Graphical Statistics, Journal of the Royal Statistical Society, and others. In addition to his academic publications, he is the author of more than thirty Harvard Business School cases, technical notes, and teaching modules, all of which focus on analytics, leadership, and decision-making. His current work also explores applications of artificial intelligence in business and education.

Research Interests

Courses

Scholarly Works

Publications

Rader K., Lipsitz, S., Fitzmaurice, G., Harrington, D., Parzen, M., and Sinha, D. “Bias-corrected estimates for logistic regression models for complex surveys with application to the United States’ Nationwide Inpatient Sample.” Statistical Methods in Medical Research 26, no. 5 (2017): 2257–2269. https://doi.org/10.1177/0962280215596550

Parzen, M., Ghosh, S., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., and Mallick, B. “A generalized linear mixed model for longitudinal binary data with a marginal logit link function.” Annals of Applied Statistics 5, no. 1 (2011): 449–467. https://doi.org/10.1214/10-AOAS390

Parzen, M., Lipsitz, S., and Metters, R. “Correlation When Data Are Missing.” Journal of the Operational Research Society 61, no. 1 (2010): 1049–1056. https://doi.org/10.1057/jors.2009.49

Labianca, J., Fairbank, J., Andrevski, G., and Parzen, M. “Striving towards the future: aspiration-performance discrepancies and planned organizational change.” Strategic Organization 7, no. 4 (2009): 433–466. https://doi.org/10.1177/1476127009349842

Bahadir, C., Bharadwaj, S., and Parzen, M. “Meta-Analysis of the Determinants of Organic Sales Growth.” International Journal of Research in Marketing 26, no. 4 (2009): 263–275. https://doi.org/10.1016/j.ijresmar.2009.06.003

Lipsitz, S., Fitzmaurice, G., Ibrahim, J., Sinha, D., Parzen, M., and Lipshultz, S. “Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: An application to AIDS data.” Journal of the Royal Statistical Society, Series A (Statistics in Society) 172, no. 1 (2009): 3–20. https://doi.org/10.1111/j.1467-985X.2008.00564.x

Fitzmaurice, G., Lipsitz, S., and Parzen, M. “Approximate Median Regression via the Box-Cox Transformation.” American Statistician 61, no. 3 (2007): 233–238. https://doi.org/10.1198/000313007X220534

Natarajan, S., Lipsitz, S., Parzen, M., and Lipshultz, S. “A measure of partial association for generalized estimating equations.” Statistical Modelling 7, no. 2 (2007): 175–190. https://doi.org/10.1177/1471082X0700700204

Parzen, M., and Lipsitz, S. “Perturbing the minimand resampling as an extension of the Bayesian bootstrap.” Statistics and Probability Letters 77, no. 6 (2007): 654–657. https://doi.org/10.1016/j.spl.2006.09.017

Parzen, M., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., Troxel, A., and Molenberghs, G. “Pseudo-Likelihood methods for the analysis of longitudinal binary data subject to nonignorable non-monotone missingness.” Journal of Data Science 5, no. 1 (2007): 1–21. https://doi.org/10.6339/JDS.2007.05(1).301

Parzen, M., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., and Troxel, A. “Pseudo-Likelihood methods for longitudinal binary data subject to nonignorable missing responses and covariates.” Statistics in Medicine 25, no. 16 (2006): 2784–2796. https://doi.org/10.1002/sim.2435

Nelson, K., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., Parzen, M., and Strawderman, R. “Use of the Probability Integral Transformation to fit nonlinear mixed-effects models with non-normal random effects.” Journal of Computational and Graphical Statistics 15, no. 1 (2006): 39–57. https://doi.org/10.1198/106186006X96854

Parzen, M., Fitzmaurice, G., and Lipsitz, S. “A note on reducing the bias of the approximate Bayesian bootstrap imputation variance estimator.” Biometrika 92, no. 4 (2005): 971–974. https://doi.org/10.1093/biomet/92.4.971

Lipsitz, S., Parzen, M., Natarajan, S., Ibrahim, J., and Fitzmaurice, G. “Generalized Linear Models with a Coarsened Covariate.” Journal of the Royal Statistical Society, Series C (Applied Statistics) 53, no. 2 (2004): 279–289. https://doi.org/10.1046/j.1467-9876.2003.05009.x

Chen, L., Wei, L. J., and Parzen, M. “Quantile Regression for Correlated Observations.” Chapter in Proceedings of the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data, edited by Danyu Lin and Patrick Heagerty. Lecture Notes in Statistics vol. 179. Springer, New York, 2003. https://doi.org/10.1007/978-1-4419-9076-1_4

Lipsitz, S., Parzen, M., Fitzmaurice, G., and Klar, N. “A Two-Stage Logistic Regression Model for Analyzing Inter-Rater Agreement.” Psychometrika 68, no. 2 (2003): 289–298. https://doi.org/10.1007/BF02294802

Horton, N., Lipsitz, S., and Parzen, M. “A Potential for Bias when Rounding in Multiple Imputation.” The American Statistician 57, no. 4 (2003): 229–233. https://doi.org/10.1198/0003130032314

Parzen, M., Lipsitz, S., and Zhao, L. P. “A Degrees-of-Freedom Approximation in Multiple Imputation.” Journal of Statistical Computation and Simulation 72, no. 4 (2002): 309–318. https://doi.org/10.1080/00949650212848

Parzen, M., Lipsitz, S., Ibrahim, J., and Lipshultz, S. “A Weighted Estimating Equation for Linear Regression with Missing Covariate Data.” Statistics in Medicine 21, no. 16 (2002): 2421–2436. https://doi.org/10.1002/sim.1195

Klar, N., Lipsitz, S., Parzen, M., and Leong, T. “An Exact Bootstrap Confidence Interval for Kappa in Small Samples.” Journal of the Royal Statistical Society, Series D (The Statistician) 51, no. 4 (2002): 1–12. https://doi.org/10.1111/1467-9884.00331

Parzen, M., Lipsitz, S., Ibrahim, J., and Klar, N. “An Estimate of the Odds Ratio that Always Exists.” Journal of Computational and Graphical Statistics 11, no. 2 (2002): 420–436. https://doi.org/10.1198/106186002760180590

Lipsitz, S., Laird, N., Brennan, T., and Parzen, M. “Estimating the Kappa-Coefficient from a Selected Sample.” Journal of the Royal Statistical Society, Series D (The Statistician) 50, no. 4 (2001): 407–16. https://www.jstor.org/stable/2681224

Lipsitz, S., Williamson, J., Klar, N., Ibrahim, J., and Parzen, M. “A Simple Method for Estimating a Regression Model for Kappa between a Pair of Raters.” Journal of the Royal Statistical Society, Series A (Statistics in Society) 164, no. 3 (2001): 449–465. https://www.jstor.org/stable/2680566

Lipsitz, S., Parzen, M., Molenberghs, G., and Ibrahim, J. “Testing for Bias in Weighted Estimating Equations with Missing Covariates.” Biostatistics 2, no. 3 (2001): 295–307. https://doi.org/10.1093/biostatistics/2.3.295

Parzen, M., and Lipsitz, S. “A Global Goodness-of-Fit Statistic for Cox Regression Models.” Biometrics 55, no. 2 (1999): 580–84. https://doi.org/10.1111/j.0006-341X.1999.00580.x

Lipsitz, S., Ibrahim, J., and Parzen, M. “A Degrees-of-Freedom Approximation for a t-Statistic with Heterogeneous Variance.” Journal of the Royal Statistical Society, Series D (The Statistician) 48, no. 4 (1999): 495–506. https://doi.org/10.1111/1467-9884.00207

Parzen, M., Lipsitz, S., and Dear, K. “Does Clustering Affect the Usual Test Statistics of No Treatment Effect in a Randomized Clinical Trial?” Biometrical Journal 40, no. 4 (1998): 385–402. https://doi.org/10.1080/10618600.1998.10474781

Lipsitz, S., Parzen, M., and Ewell, M. “Inference Using Conditional Logistic Regression with Missing Covariates.” Biometrics 54, no. 1 (1998): 295–303. https://doi.org/10.2307/2534015

Strawderman, R., Parzen, M., and Wells, M. “Accurate Confidence Limits for Survivor Function Quantiles under Random Censoring.” Biometrics 53, no. 4 (1997): 1399–1415. https://doi.org/10.2307/2533506

Parzen, M., Wei, L. J., and Ying, Z. “Simultaneous Confidence Intervals for the Difference of Two Survival Functions.” Scandinavian Journal of Statistics 24, no. 3 (1997): 309–14. http://www.jstor.org/stable/4616457

Fiebig, C., Hayes, C. H., and Parzen, M. “Development of expertise in complex domains.” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics vol. 3 (1997): 2684–2689. https://doi.org/10.1109/ICSMC.1997.635341

Hayes, C. H., and Parzen, M. “QUEM: An Achievement Test for Knowledge-Based Systems.” IEEE Transactions on Knowledge and Data Engineering 9, no. 6 (1997): 838–43. https://doi.org/10.1109/69.649311

Rudberg, M. A., Parzen, M., Leonard, L., and Cassel, C. K. “Functional Limitation Pathways and Transitions in Older Persons.” The Gerontologist 36, no. 4 (1996): 430–440. https://doi.org/10.1093/geront/36.4.430

Lipsitz, S., and Parzen, M. “A Jackknife Estimator of Variance for Cox Regression for Correlated Survival Data.” Biometrics 52, no. 1 (1996): 291–98. https://doi.org/10.2307/2533164

Lipsitz, S., and Parzen, M. “Sample Size Calculations for Non-Randomized Studies.” Journal of the Royal Statistical Society, Series D (The Statistician) 44, no. 1 (1995): 81–90. https://doi.org/10.2307/2348619

Parzen, M., Wei, L. J., and Ying, Z. “A Resampling Method Based on Pivotal Estimating Functions.” Biometrika 81, no. 2 (1994): 341–350. https://doi.org/10.1093/biomet/81.2.341

Harvard Business School Cases and Technical Notes

Hamid, Zareef, and Michael Parzen. “Reimagining The MBA in an AI World (B)” HBS Case 625-125, June 2025.

Hamid, Zareef, and Michael Parzen. “Reimagining The MBA in an AI World (A)” HBS Case 625-107, June 2025.

Parzen, Michael. “FinSecure Bank: Charting an AI Course – Build or Buy?” HBS Case 625-126, May 2025.

Parzen, Michael. “Innovatech Solutions (B): The AI Curveball.” HBS Case 625-123, May 2025.

Parzen, Michael. “Innovatech Solutions (A): The AI Co-Pilot – A Test of Generative Leadership.” HBS Case 625-122, May 2025.
Ellery, Jo, and Michael Parzen. “Introduction to Generative AI” HBS Case 625-096, October 2024.

Ellery, Jo, and Michael Parzen. “Prompt Engineering” HBS Case 625-056, October 2024.

Ellery, Jo, and Michael Parzen. “PCA for MBAs” HBS Case 625-066, September 2024.

Ellery, Jo, and Michael Parzen. “Introduction to Association Rule Learning” HBS Case 625-065, September 2024.

Ellery, Jo, and Michael Parzen. “Unsupervised Natural Language Processing” HBS Case 625-064, September 2024.

Ellery, Jo, and Michael Parzen. “Introduction to SQL in Python” HBS Case 625-024, August 2024.

Ellery, Jo, and Michael Parzen. “Version Control and Web Development.” HBS Case 625-018, August 2024.

Ellery, Jo, and Michael Parzen. “Introduction to Optimization in Python.” HBS Case 625-017, July 2024.

Ellery, Jo, and Michael Parzen. “Introduction to Data Analysis in Python.” HBS Case 625-016, July 2024.

Ellery, Jo, and Michael Parzen. “What is AI?” HBS Case 625-010, July 2024.

Ellery, Jo, and Michael Parzen. “Algorithmic Thinking.” HBS Case 624-104, June 2024.

Srinivasan, Suraj, Michael Parzen, and Radhika Kak. “Coursera’s Foray into GenAI.” HBS Case 124-089, March 2024.

Parzen, Michael, Marily Nika, and Jessie Li. “AI Product Development Lifecycle.” HBS Case 624-070, January 2024.

Parzen, Michael, Michael Toffel, Susan Pinckney, and Amram Migdal. “Arla Foods: Data Driven Decarbonization.” HBS Case 624-022, August 2023.

Parzen, Michael, Alexander Farrow, Paul Hamilton, and Jessie Li. “Sparking Innovation in the United States Air Force.” HBS Case 624-002, July 2023.

Parzen, Michael, Eddie Lin, Douglas Ng, and Jessie Li. “Fizzy Fusion: When Data-Driven Decision Making Failed.” HBS Case 623-071, April 2023.

Parzen, Michael, Eddie Lin, Douglas Ng, and Jessie Li. “An Art & a Science: How to Apply Design Thinking to Data Science Challenges.” Harvard Business School Technical Note 623-070, April 2023.

Bojinov, Iavor, Michael Parzen, and Paul Hamilton. “On Ramp to Crypto.” Harvard Business School Case 623-040, October 2022.

Bojinov, Iavor I., and Michael Parzen. “History of the Cola Wars.” Harvard Business School Case 623-029, October 2022.

Bojinov, Iavor, Michael Parzen, and Paul Hamilton. “Causal Inference.” Harvard Business School Module Note 622-111, June 2022.

Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. “Prediction & Machine Learning.” Harvard Business School Module Note 622-101, March 2022.

Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. “Linear Regression.” Harvard Business School Module Note 622-100, March 2022.

Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. “Statistical Inference.” Harvard Business School Module Note 622-099, March 2022.

Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. “Exploratory Data Analysis.” Harvard Business School Module Note 622-098, March 2022.

Bojinov, Iavor I., and Michael Parzen. “Data Science at the Warriors.” Harvard Business School Case 622-048, August 2021.

Parzen, Michael, and Paul J. Hamilton. “Introduction to Linear Regression.” Harvard Business School Technical Note 621-086, June 2021.

Parzen, Michael, Natalie Epstein, Chiara Farronato, and Michael Toffel. “T-tests: Theory and Practice.” Harvard Business School Tutorial 621-707, February 2021.

Parzen, Michael, and Paul J. Hamilton. “Probability Distributions.” Harvard Business School Technical Note 621-704, February 2021.

Parzen, Michael, and Paul J. Hamilton. “The FIRE Savings Calculator.” Harvard Business School Case 621-087, January 2021.

Grushka-Cockayne, Yael, Michael Parzen, Paul Hamilton, and Steven Randazzo. “Kaggle 2019 Data Science Survey.” Harvard Business School Case 620-091, January 2020.

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