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)

Shmueli, G., "Wavelet-based Monitoring in Modern Biosurveillance", Working Paper RHS-06-002: Smith School of Business, University of Maryland, 2005.

Dillard, B. L., and G. Shmueli, "Wavelet-Based Monitoring for Disease Outbreaks and Bioterrorism: Methods and Challenges", InterStat, vol. 3, issue March, 2010.

Shmueli, G., "Wavelet-Based Monitoring for Biosurveillance", Axioms, vol. 2, issue 3, pp. 345-370, 2013.

Lotze, T., G. Shmueli, S. Murphy, and H. S. Burkom, "A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks", Proceedings of the 23rd International Conference on Machine Learning (ICML), Workshop on Machine Learning Algorithms for Surveillance and Event Detection, Pittsburgh, PA, 2006.

Shmueli, G., and W. Jank, "Visualizing Online Auctions", Journal of Computational and Graphical Statistics, vol. 14, issue 2, pp. 299-319, 2005. JCGS Visualizing Online Auctions.pdf (923.45 KB)

Jank, W., G. Shmueli, C. Plaisant, and B. Shneiderman, "Visualizing Functional Data with an Application to eBays Online Auctions", Handbook on Computational Statistics on Data Visualization, Heidelberg, Springer, 2008. CSC-Visualizing-FDA.pdf (1.14 MB)

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., 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)

Fu, J. C., G. Shmueli, and Y. M. Chang, "A Unified Markov Chain Approach for Computing the Run Length Distribution in Control Charts with Simple or Compound Rules", Statistics & Probability Letters, vol. 65, issue 4, pp. 457-466, 2003. StatProbLettersPaper.pdf (246.79 KB)

Chatla, S.B., and G. Shmueli, "A Tree-Based Semi-Varying Coefficient Model for the COM-Poisson Distribution", Journal of Computational and Graphical Statistics, vol. 29, issue 4, pp. 827-846, 2020. JCGS 2020 Chatla Shmueli Tree based Semi-Varying Coefficient Model for CMP.pdf (4.29 MB)

Yahav, I., G. Shmueli, and D. Mani, "A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data", MIS Quarterly, vol. 40, issue 4, pp. 819-848, 2016.

Yahav, I., G. Shmueli, and D. Mani, "A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data", MIS Quarterly, vol. 40, issue 4, pp. 819-848, 2016.

Gupta, R., D. Mani, S. Mithas, and G. Shmueli, "Tree Matching Solution for Self-Selection in Impact Surveys", Statistical Challenges in Ecommerce Research (SCECR), Montreal, Canada, 28/06/2012. SCECR 2012 Poster Tree Matching for Self-Selection in Impact Surveys.pdf (807.52 KB)

Shmueli, G., W. Jank, and V. Hyde, "Transformations for Semi-Continuous Data", Computational Statistics & Data Analysis, vol. 52, issue 8, pp. 4000-4020, 2008. CSDA-semiContinuousData.pdf (1.51 MB)

Shmueli, G., W. Jank, and V. Hyde, "Transformations for Semi-Continuous Data", Working Paper RHS 06-051: Smith School of Business, University of Maryland, 2006.

Lin, M., H. C. Lucas, and G. Shmueli, "Too Big to Fail: Larger Samples and False Discoveries", Working Paper RHS 06-068: Smith School of Business, University of Maryland, 2009.

Lin, M., H. C. Lucas, and G. Shmueli, "Too Big To Fail: Large Samples and the P-Value Problem", Information Systems Research, vol. 24, issue 4, pp. 906-917, 2013. Article (269.29 KB)

Shmueli, G., "To Explain or To Predict?", Statistical Science, vol. 25, issue 3, pp. 289-310, 2010. Stat Science published.pdf (293.36 KB)

Shmueli, G., "To Explain or To Predict?", Working Paper (RHS 06-099) Smith School of Business, University of Maryland, 2009.