Sharma, P., M. Sarstedt, G. Shmueli, K. H. Kim, and K. O. Thiele, "PLS-based model selection: the role of alternative explanations in information systems research", Journal of the Association for Information Systems, vol. 20, issue 4, 2019.

Shmueli, G., and O. Koppius, "Predictive Analytics in Information Systems Research", MIS Quarterly, vol. 35, issue 3, pp. 553-572, 2011. MISQ-Predictive-Analytics-in-IS-Shmueli-Koppius -2011.pdf (303.74 KB)

Shmueli, G., and O. Koppius, "Predictive Analytics in Information Systems Research", ERN Economics of Networks eJournal, vol. 2, issue 56, 2010.

Shmueli, G., and O. Koppius, "Predictive Analytics in Information Systems Research", Working Paper RHS 06-138: Smith School of Business, University of Maryland, 2010.

Shmueli, G., and O. Koppius, "Predictive vs. Explanatory Modeling in IS Research", Conference on Information Systems & Technology (Best Paper Award), Seattle, WA, 03/11/2007.

Kenett, R. S., and G. Shmueli, "Rejoinder: Helping authors and reviewers ask the right questions: The InfoQ framework for reviewing applied research", Statistical Journal of the IAOS, vol. 32, issue 1, pp. 33-35, 2016.

Kenett, R. S., and G. Shmueli, "Rejoinder: On Information Quality", JRSS A, vol. 177, issue 1, pp. 35-38, 2014.

Lotze, T., G. Shmueli, and I. Yahav, "Simulating and Evaluating Biosurveillance Datasets", Biosurveillance: Methods and Case Studies: Chapman and Hall, 2010. Book Chapter (1.93 MB)

Kenett, R. S., and G. Shmueli, "A special issue on: Actual impact and future perspectives on stochastic modelling in business and industry", Applied Stochastic Models in Business and Industry, vol. 31, issue 1, pp. 1-2, 2015.

Shmueli, G., T. P. Minka, J. B. Kadane, S. Borle, and P. Boatwright, "A Useful Distribution for Fitting Discrete Data: Revival of the COM-Poisson", Journal of The Royal Statistical Society, Series C (Applied Statistics), vol. 54, issue 1, pp. 127-142, 2005. JRSS-COM-Poisson.pdf (278.49 KB)

Shmueli, G., T. P. Minka, J. B. Kadane, S. Borle, and P. Boatwright, "Using Computational and Mathematical Methods to Explore a New Distribution: The v-Poisson", Technical Report #740: Dept. of Statistics, Carnegie Mellon University, 2001.

Shmueli, G., and O. Koppius, "What is Predictive about Partial Least Squares?", Sixth Symposium on Statistical Challenges in eCommerce Research (SCECR), University of Texas at Austin, McCombs School of Business, TX, 05/06/2010. Conference Paper (285.09 KB)