Rettinger, F., W. Jank, G. Tutz, and G. Shmueli, "Smoothing Sparse and Unevenly-Sampled Curves using Semiparametric Mixed Models: An Application to Online Auctions", Journal of The Royal Statistical Society, Series C (Applied Statistics), vol. 57, issue 2, pp. 127-148, 2008. JRSSC.57.2 (2008) 127-148.pdf (1.39 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.

Jank, W., and G. Shmueli, "A Special Issue on Statistical Challenges and Opportunities in Electronic Commerce Research", Statistical Science, vol. 21, issue 2, pp. 113-115, 2006.

Shmueli, G., and H. S. Burkom, "Statistical Challenges Facing Early Outbreak Detection in Biosurveillance", Technometrics (Special Issue on Anomaly Detection), vol. 52, issue 1, pp. 39-51, 2010. Previous version ("Statistical Challenges in Modern Biosurveillance") (381.03 KB) Published Version (751.05 KB)

Jank, W., G. Shmueli, M. Dass, I. Yahav, and S. Zhang, "Statistical Challenges in eCommerce: Modeling Dynamic and Networked Data", Tutorials in Operations Research: INFORMS, 2008. INFORMS Tutorials-2008-CD-chapter03.pdf (711.96 KB)

Shmueli, G., "Statistical Inference with Large (eCommerce) Datasets", Working Paper RHS 06-61: Smith School of Business, University of Maryland, 2008.

Fienberg, S. E., and G. Shmueli, "Statistical Issues and Challenges Associated with Rapid Detection of Bio-terrorist Attacks", Statistics in Medicine, vol. 24, issue 4, pp. 513-29, 2005. FienbergShmueli-SIM-2005.pdf (122.99 KB)

Jank, W., and G. Shmueli, "Statistical Methods in eCommerce Research", Statistics in Practice: John Wiley & Sons, 2008.

Jank, W., and G. Shmueli, "Studying Heterogeneity of Price Evolution in eBay Auctions Via Functional Clustering", Business Computing, vol. 3: Emerald, pp. 237-261, 2009.

Shmueli, G., "System-Wide Probabilities for Systems with Runs and Scans Rules", Methodology and Computing in Applied Probability, vol. 4, pp. 401-419, 2003. Mcap_5118753.pdf (989.79 KB)

Shmueli, G., and C. Soares, "Teaching Data Mining in the Business School: Experience from Three Continents", Teaching Machine Learning Workshop, ICML, Edinburgh, Scotland, UK, June 2012. ICML 2012 Teaching BS vs CS.pdf (413.83 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.

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)

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.

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.

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)

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.