A Novel Weak Estimator For Dynamic Systems

In this paper, we propose a novel approach for classifying incoming continuous data under a non-stationary environment. A class of estimators termed stochastic learning weak estimators has been generalized to include continuous time sampling and countable state categories. The method is founded on non-stationary Markov chain techniques and is useful in diverse applications, such as consumer behavior analysis, e-mail spam classification, or understanding drug effectiveness. In terms of tracking the true state probabilities, these weak estimators consistently outperform traditional competitors such as maximum likelihood estimates. Only one user defined parameter is necessary and the method is free of subjective “moving window” type algorithms. We have conducted extensive simulations and real data analyses for classification purposes.