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.

Lotze, T., and G. Shmueli, "On the relationship between forecast accuracy and detection performance: An application to biosurveillance", 2008 IEEE Conference on Technologies for Homeland Security, Boston, MA, 2008. ieee.pdf (243.68 KB)

Lotze, T., S. Murphy, and G. Shmueli, "Preparing Biosurveillance Data for Classic Monitoring", Advances in Disease Surveillance, vol. 6, 2008. DataPreparation AdvDisSurv.pdf (470.11 KB)

Lotze, T., and G. Shmueli, "Ensemble Forecasting for Disease Outbreak Detection", 23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL, 2008. AAAI7LotzeT.pdf (61 KB)

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)

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.

Lotze, T., and G. Shmueli, "How does improved forecasting benefit detection? An application to biosurveillance", International Journal on Forecasting, vol. 25, pp. 467-483, 2009. IJF2009 ForecastingBiosurveillance.pdf (2.19 MB)