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)

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)

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.

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., 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)

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

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

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)