First Topic: Univariate Statistical Methods

Our theme for the first round of the class will be Univariate Statistical Methods. Participants are free to choose any reading within this theme that they want, but as a starter for what types of things you could do, I have condensed a set of subtopics and possible reading sources taken from http://rods.health.pitt.edu/LIBRARY/2002%20Moore%20Summary%20of%20Biosurveillance%20relevant.pdf.

Here is the list:

Serfling’s Method.

• Serfling, R. E. (1963). Methods for Current Statistical Analysis of Excess Pneumonia-Influenza Deaths. Publich Health Reports, 78, 494–506.
• Tsui, F. C. R., Wagner, M., Dato, V., & Chang, H. C. (2001). Value of ICD-9–Coded Chief Complaints for Detection of Epidemics. In Symposium of Journal of American Medical Informatics Association.

ARMA/ARIMA/SARIMA

• Box, G., Jenkins, G., & Reinsel, G. (1994). Time Series Analysis: Forecasting and Control, 3rd Ed. Englewood Cliffs, NJ: Prentice Hall.
• Hamilton, J. (1994). Time Series Analysis. Princeton University Press. 13

HMMS/Kalman Filter

• Rabiner, L. R. (1989). A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE, 77(2), 257–285.
• Hamilton, J. (1994). Time Series Analysis. Princeton University Press. 13
  

Recursive Least Square (RLS) adaptive Filter

CUSUM/ACUSUM

• Bos, T., & Fetherston, T. A. (1992). Market Model Nonstationarity in the Korean Stock Market. In Pacific-Basin Capital Markets Research, Vol. 3, pp. 287–301. Elsevier Science Publishers B. V. (North-Holland), Amsterdam.

Change-point Detection

• Carlstein, E. (1988). Nonparametric Change-point Estimation. The Annals of Statistics, 16(1), 188–197.