Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts

Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts

Automated public health records provide the necessary data for rapid outbreak detection. An adaptive exponentially weighted moving average EWMA plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in counts and provides “one-day-ahead” forec...

Observation Driven Models for Poisson Counts

This paper is concerned with a general class of observation driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modeling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which t...

Test for Spatio-Temporal Counts Being Poisson

Improving Big Plans

Past research on assessing and improving plans in domains that contain uncertainty has focused on analytic techniques that are exponential in the length of the plan. Little work has been done on choosing from among the many ways in which a plan can be improved. We present the IMPROVE algorithm which simulates the execution of large, probabilistic plans. IMPROVE runs a data mining algorithm on t...

Multivariate Poisson Hidden Markov Models for Analysis of Spatial Counts