Drawing on three decades of experience in applied business analytics, data analysis, and statistical modeling, Dr. Parzen\u2019s 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.<\/p>\n
Prior to joining Boston University\u2019s Metropolitan College, Dr. Parzen held full-time faculty positions at Harvard Business School, Harvard College, Emory University\u2019s Goizueta Business School, and the University of Chicago\u2019s Booth School of Business. He has also played key roles in curriculum design and program leadership, earning recognition for teaching excellence across institutions.<\/p>\n
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<\/em>, Journal of Computational and Graphical Statistics<\/em>, Journal of the Royal Statistical Society<\/em>, 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.<\/p>\n <\/div>\n<\/div>\n\n <\/p>\n <\/div><\/ul>\n <\/div>\n<\/div>\n\n Rader K., Lipsitz, S., Fitzmaurice, G., Harrington, D., Parzen, M., and Sinha, D. \u201cBias-corrected estimates for logistic regression models for complex surveys with application to the United States’ Nationwide Inpatient Sample.\u201d Statistical Methods in Medical Research<\/em> 26, no. 5 (2017): 2257\u20132269. https:\/\/doi.org\/10.1177\/0962280215596550<\/a><\/p>\n Parzen, M., Ghosh, S., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., and Mallick, B. \u201cA generalized linear mixed model for longitudinal binary data with a marginal logit link function.\u201d Annals of Applied Statistics<\/em> 5, no. 1 (2011): 449\u2013467. https:\/\/doi.org\/10.1214\/10-AOAS390<\/a> <\/p>\n Parzen, M., Lipsitz, S., and Metters, R. \u201cCorrelation When Data Are Missing.\u201d Journal of the Operational Research Society<\/em> 61, no. 1 (2010): 1049\u20131056. https:\/\/doi.org\/10.1057\/jors.2009.49<\/a> <\/p>\n Labianca, J., Fairbank, J., Andrevski, G., and Parzen, M. \u201cStriving towards the future: aspiration-performance discrepancies and planned organizational change.\u201d Strategic Organization<\/em> 7, no. 4 (2009): 433\u2013466. https:\/\/doi.org\/10.1177\/1476127009349842<\/a><\/p>\n Bahadir, C., Bharadwaj, S., and Parzen, M. \u201cMeta-Analysis of the Determinants of Organic Sales Growth.\u201d International Journal of Research in Marketing <\/em>26, no. 4 (2009): 263\u2013275. https:\/\/doi.org\/10.1016\/j.ijresmar.2009.06.003<\/a><\/p>\n Lipsitz, S., Fitzmaurice, G., Ibrahim, J., Sinha, D., Parzen, M., and Lipshultz, S. \u201cJoint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: An application to AIDS data.\u201d Journal of the Royal Statistical Society, Series A (Statistics in Society)<\/em> 172, no. 1 (2009): 3\u201320. https:\/\/doi.org\/10.1111\/j.1467-985X.2008.00564.x<\/a><\/p>\n Fitzmaurice, G., Lipsitz, S., and Parzen, M. \u201cApproximate Median Regression via the Box-Cox Transformation.\u201d American Statistician<\/em> 61, no. 3 (2007): 233\u2013238. https:\/\/doi.org\/10.1198\/000313007X220534<\/a><\/p>\n Natarajan, S., Lipsitz, S., Parzen, M., and Lipshultz, S. \u201cA measure of partial association for generalized estimating equations.\u201d Statistical Modelling<\/em> 7, no. 2 (2007): 175\u2013190. https:\/\/doi.org\/10.1177\/1471082X0700700204<\/a><\/p>\n Parzen, M., and Lipsitz, S. \u201cPerturbing the minimand resampling as an extension of the Bayesian bootstrap.\u201d Statistics and Probability Letters<\/em> 77, no. 6 (2007): 654\u2013657. https:\/\/doi.org\/10.1016\/j.spl.2006.09.017<\/a><\/p>\n Parzen, M., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., Troxel, A., and Molenberghs, G. \u201cPseudo-Likelihood methods for the analysis of longitudinal binary data subject to nonignorable non-monotone missingness.\u201d Journal of Data Science<\/em> 5, no. 1 (2007): 1\u201321. https:\/\/doi.org\/10.6339\/JDS.2007.05(1).301<\/a><\/p>\n Parzen, M., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., and Troxel, A. \u201cPseudo-Likelihood methods for longitudinal binary data subject to nonignorable missing responses and covariates.\u201d Statistics in Medicine<\/em> 25, no. 16 (2006): 2784\u20132796. https:\/\/doi.org\/10.1002\/sim.2435<\/a><\/p>\n Nelson, K., Lipsitz, S., Fitzmaurice, G., Ibrahim, J., Parzen, M., and Strawderman, R. \u201cUse of the Probability Integral Transformation to fit nonlinear mixed-effects models with non-normal random effects.\u201d Journal of Computational and Graphical Statistics<\/em> 15, no. 1 (2006): 39\u201357. https:\/\/doi.org\/10.1198\/106186006X96854<\/a><\/p>\n Parzen, M., Fitzmaurice, G., and Lipsitz, S. \u201cA note on reducing the bias of the approximate Bayesian bootstrap imputation variance estimator.\u201d Biometrika<\/em> 92, no. 4 (2005): 971\u2013974. https:\/\/doi.org\/10.1093\/biomet\/92.4.971<\/a><\/p>\n Lipsitz, S., Parzen, M., Natarajan, S., Ibrahim, J., and Fitzmaurice, G. \u201cGeneralized Linear Models with a Coarsened Covariate.\u201d Journal of the Royal Statistical Society, Series C (Applied Statistics)<\/em> 53, no. 2 (2004): 279\u2013289. https:\/\/doi.org\/10.1046\/j.1467-9876.2003.05009.x<\/a><\/p>\n Chen, L., Wei, L. J., and Parzen, M. \u201cQuantile Regression for Correlated Observations.\u201d Chapter in Proceedings of the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data<\/em>, edited by Danyu Lin and Patrick Heagerty. Lecture Notes in Statistics<\/em> vol. 179. Springer, New York, 2003. https:\/\/doi.org\/10.1007\/978-1-4419-9076-1_4<\/a><\/p>\n Lipsitz, S., Parzen, M., Fitzmaurice, G., and Klar, N. \u201cA Two-Stage Logistic Regression Model for Analyzing Inter-Rater Agreement.\u201d Psychometrika<\/em> 68, no. 2 (2003): 289\u2013298. https:\/\/doi.org\/10.1007\/BF02294802<\/a><\/p>\n Horton, N., Lipsitz, S., and Parzen, M. \u201cA Potential for Bias when Rounding in Multiple Imputation.\u201d The American Statistician<\/em> 57, no. 4 (2003): 229\u2013233. https:\/\/doi.org\/10.1198\/0003130032314<\/a><\/p>\n Parzen, M., Lipsitz, S., and Zhao, L. P. \u201cA Degrees-of-Freedom Approximation in Multiple Imputation.\u201d Journal of Statistical Computation and Simulation<\/em> 72, no. 4 (2002): 309\u2013318. https:\/\/doi.org\/10.1080\/00949650212848<\/a><\/p>\n Parzen, M., Lipsitz, S., Ibrahim, J., and Lipshultz, S. \u201cA Weighted Estimating Equation for Linear Regression with Missing Covariate Data.\u201d Statistics in Medicine<\/em> 21, no. 16 (2002): 2421\u20132436. https:\/\/doi.org\/10.1002\/sim.1195<\/a><\/p>\n Klar, N., Lipsitz, S., Parzen, M., and Leong, T. \u201cAn Exact Bootstrap Confidence Interval for Kappa in Small Samples.\u201d Journal of the Royal Statistical Society, Series D (The Statistician)<\/em> 51, no. 4 (2002): 1\u201312. https:\/\/doi.org\/10.1111\/1467-9884.00331<\/a><\/p>\n Parzen, M., Lipsitz, S., Ibrahim, J., and Klar, N. \u201cAn Estimate of the Odds Ratio that Always Exists.\u201d Journal of Computational and Graphical Statistics<\/em> 11, no. 2 (2002): 420\u2013436. https:\/\/doi.org\/10.1198\/106186002760180590<\/a><\/p>\n Lipsitz, S., Laird, N., Brennan, T., and Parzen, M. \u201cEstimating the Kappa-Coefficient from a Selected Sample.\u201d Journal of the Royal Statistical Society, Series D (The Statistician)<\/em> 50, no. 4 (2001): 407\u201316. https:\/\/www.jstor.org\/stable\/2681224<\/a><\/p>\n Lipsitz, S., Williamson, J., Klar, N., Ibrahim, J., and Parzen, M. \u201cA Simple Method for Estimating a Regression Model for Kappa between a Pair of Raters.\u201d Journal of the Royal Statistical Society, Series A (Statistics in Society)<\/em> 164, no. 3 (2001): 449\u2013465. https:\/\/www.jstor.org\/stable\/2680566<\/a><\/p>\n Lipsitz, S., Parzen, M., Molenberghs, G., and Ibrahim, J. \u201cTesting for Bias in Weighted Estimating Equations with Missing Covariates.\u201d Biostatistics<\/em> 2, no. 3 (2001): 295\u2013307. https:\/\/doi.org\/10.1093\/biostatistics\/2.3.295<\/a><\/p>\n Parzen, M., and Lipsitz, S. \u201cA Global Goodness-of-Fit Statistic for Cox Regression Models.\u201d Biometrics<\/em> 55, no. 2 (1999): 580\u201384. https:\/\/doi.org\/10.1111\/j.0006-341X.1999.00580.x<\/a><\/p>\n Lipsitz, S., Ibrahim, J., and Parzen, M. \u201cA Degrees-of-Freedom Approximation for a t-Statistic with Heterogeneous Variance.\u201d Journal of the Royal Statistical Society, Series D (The Statistician)<\/em> 48, no. 4 (1999): 495\u2013506. https:\/\/doi.org\/10.1111\/1467-9884.00207<\/a><\/p>\n Parzen, M., Lipsitz, S., and Dear, K. \u201cDoes Clustering Affect the Usual Test Statistics of No Treatment Effect in a Randomized Clinical Trial?\u201d Biometrical Journal<\/em> 40, no. 4 (1998): 385\u2013402. 3.0.CO;2-%23″ rel=”noopener” target=”_blank”>https:\/\/doi.org\/10.1002\/(SICI)1521-4036(199808)40:4<385::AID-BIMJ385>3.0.CO;2-%23<\/a><\/p>\n Lipsitz, S., Parzen, M., and Molenberghs, G. \u201cObtaining the Maximum Likelihood Estimates in Incomplete RxC Contingency Tables Using a Poisson Generalized Linear Model.\u201d Journal of Computational and Graphical Statistics<\/em> 7, no. 3 (1998): 356\u201376. https:\/\/doi.org\/10.1080\/10618600.1998.10474781<\/a><\/p>\n Lipsitz, S., Parzen, M., and Ewell, M. \u201cInference Using Conditional Logistic Regression with Missing Covariates.\u201d Biometrics<\/em> 54, no. 1 (1998): 295\u2013303. https:\/\/doi.org\/10.2307\/2534015<\/a><\/p>\n Strawderman, R., Parzen, M., and Wells, M. \u201cAccurate Confidence Limits for Survivor Function Quantiles under Random Censoring.\u201d Biometrics<\/em> 53, no. 4 (1997): 1399\u20131415. https:\/\/doi.org\/10.2307\/2533506<\/a><\/p>\n Parzen, M., Wei, L. J., and Ying, Z. \u201cSimultaneous Confidence Intervals for the Difference of Two Survival Functions.\u201d Scandinavian Journal of Statistics<\/em> 24, no. 3 (1997): 309\u201314. http:\/\/www.jstor.org\/stable\/4616457<\/a><\/p>\n Fiebig, C., Hayes, C. H., and Parzen, M. \u201cDevelopment of expertise in complex domains.\u201d Proceedings of the IEEE International Conference on Systems, Man and Cybernetics<\/em> vol. 3 (1997): 2684\u20132689. https:\/\/doi.org\/10.1109\/ICSMC.1997.635341<\/a><\/p>\n Hayes, C. H., and Parzen, M. \u201cQUEM: An Achievement Test for Knowledge-Based Systems.\u201d IEEE Transactions on Knowledge and Data Engineering<\/em> 9, no. 6 (1997): 838\u201343. https:\/\/doi.org\/10.1109\/69.649311<\/a><\/p>\n Rudberg, M. A., Parzen, M., Leonard, L., and Cassel, C. K. \u201cFunctional Limitation Pathways and Transitions in Older Persons.\u201d The Gerontologist<\/em> 36, no. 4 (1996): 430\u2013440. https:\/\/doi.org\/10.1093\/geront\/36.4.430<\/a><\/p>\n Lipsitz, S., and Parzen, M. \u201cA Jackknife Estimator of Variance for Cox Regression for Correlated Survival Data.\u201d Biometrics <\/em>52, no. 1 (1996): 291\u201398. https:\/\/doi.org\/10.2307\/2533164<\/a> <\/p>\n Lipsitz, S., and Parzen, M. \u201cSample Size Calculations for Non-Randomized Studies.\u201d Journal of the Royal Statistical Society, Series D (The Statistician)<\/em> 44, no. 1 (1995): 81\u201390. https:\/\/doi.org\/10.2307\/2348619<\/a><\/p>\nResearch Interests<\/h2>
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Courses<\/h2>
Scholarly Works<\/h2>
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