Bibliography

Found 163 results
[ Title(Asc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
W
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. PDF icon 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.
V
Shmueli, G., and W. Jank, "Visualizing Online Auctions", Journal of Computational and Graphical Statistics, vol. 14, issue 2, pp. 299-319, 2005. PDF icon 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. PDF icon CSC-Visualizing-FDA.pdf (1.14 MB)
U
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. PDF icon 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. PDF icon StatProbLettersPaper.pdf (246.79 KB)
T
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. PDF icon 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", Working Paper RHS 06-051: Smith School of Business, University of Maryland, 2006.
Shmueli, G., W. Jank, and V. Hyde, "Transformations for Semi-Continuous Data", Computational Statistics & Data Analysis, vol. 52, issue 8, pp. 4000-4020, 2008. PDF icon CSDA-semiContinuousData.pdf (1.51 MB)
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. PDF icon Article (269.29 KB)
Shmueli, G., "To Explain or To Predict?", Statistical Science, vol. 25, issue 3, pp. 289-310, 2010. PDF icon 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.
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. PDF icon ICML 2012 Teaching BS vs CS.pdf (413.83 KB)

Pages