Biology Reference
In-Depth Information
Friedman, J. H. and L. C. Rafsky (1979). Multivariate generalizations of the Wald-
Wolfowitz and Smirnov two-sample tests.
The Annals of Statistics
7(4)
, 697-717.
Goldenberg, A., G. Shmueli, R. A. Caruana, and S. E. Fienberg (2002). Early statistical
detection of anthrax outbreaks by tracking over-the-counter medication sales.
Proceeding of the National Academy of Sciences
99,
5237-5240.
Hall, P. and N. Tajvidi (2002). Permutation tests for equality of distributions in high-
dimensional settings.
Biometrika
89(2)
, 359-374.
Henze, N. (1988). A multivariate two-sample test based on the number of nearest
neighbor type coincidences.
The Annals of Statistics
16(2)
, 772-783.
Hutwagner, L., T. Browne, G. M. Seeman, and A. T. Fleischauer (2005). Comparing aber-
ration detection methods with simulated data.
Emerging Infectious Diseases
Feb
.
Kim, K.-K. and R. V. Foutz (1987). Tests for the multivariate two-sample problem
based on empirical probability measures.
The Canadian Journal of Statistics / La
Revue Canadienne de Statistique
15(1)
, 41-51.
Lotze, T., S. P. Murphy, and G. Shmueli (2008). Preparing biosurveillance data for clas-
sic monitoring.
Advances in Disease Surveillance
.
Mandl, K., B. Reis, and C. Cassa (2004). Measuring outbreak-detection performance
by using controlled feature set simulations.
Morbidity and Mortality Weekly Report
53
Suppl
, 130-136.
Reis, B. and K. Mandl (2003). Time series modeling for syndromic surveillance.
BMC
Medical Informatics and Decision Making
3
(2).
Reis, B. Y., M. Pagano, and K. D. Mandl (2003). Using temporal context to improve bio-
surveillance.
Proceedings of the National Academy of Sciences
100(4)
, 1961-1965.
Rolka, H. (2006).
Statistical Methods in Counter-Terrorism: Game Theory, Modeling,
Syndromic Surveillance and Biometric Authentication
, Chapter Emerging Public
Health Biosurveillance Directions, pp. 101-107. Springer.
Schilling, M. F. (1986). Multivariate two-sample tests based on nearest neighbors.
Journal of the American Statistical Association
81(395)
, 799-806.
Shmueli, G., and Burkom, H. S. (2010). Statistical Challenges Facing Early Outbreak
Detection in Biosureveillance.
Technometrics
Vol 52 (1), pp. 39-51.
Siddiqi, S. M., B. Boots, G. J. Gordon, and A. W. Dubrawski (2007). Learning sta-
ble multivariate baseline models for outbreak detection.
Advances in Disease
Surveillance
4
, 266.
Siegrist, D. and J. Pavlin (2004). Bio-ALIRT biosurveillance detection algorithm evalu-
ation.
Morbidity and Mortality Weekly Reports (MMWR)
53
(suppl)
, 152-158.
Stoto, M., R. D. Fricker, A. Jain, J. O. Davies-Cole, C. Glymph, G. Kidane, G. Lum, L.
Jones, K. Dehan, and C. Yuan (2006). Evaluating statistical methods for syndro-
mic surveillance. In A. Wilson, G. Wilson, and D. H. Olwell (Eds.),
Statistical
Methods in Counter-Terrorism: Game Theory, Modeling, Syndromic Surveillance and
Biometric Authentication
, pp. 141-172. ASA-SIAM.
Wallstrom, G. L., M. Wagner, and W. Hogan (2005). High-fidelity injection detectabil-
ity experiments: A tool for evaluating syndromic surveillance systems.
Morbidity
and Mortality Weekly Report
54
Suppl
, 85-91.
Watkins, R. E., S. Eagleson, S. Beckett, G. Garner, B. Veenendaal, G. Wright, and
A. J. Plant (2007). Using gis to create synthetic disease outbreaks.
BMC Medical
Informatics and Decision Making
7:4
.
Wong, W.-K., A. Moore, G. Cooper, and M. Wagner (2002). Rule-based anomaly pat-
tern detection for detecting disease outbreaks. In
Proceedings of the 18th National
Conference on Artificial Intelligence
. MIT Press.
