Title

A Novel Weak Estimator for Dynamic Systems

Document Type

Conference Proceeding

Publication Date

12-4-2017

Publication Title

IEEE Access

Volume

5

First page number:

27354

Last page number:

27365

Abstract

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.

Keywords

Stochastic learning weak estimators, Dynamic systems, Continuous time Markov chain, Countable state space, Non-stationarity, Classification, Simulation.

Language

eng

UNLV article access

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