JSM 2005 - Toronto

Abstract #304763

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 458
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #304763
Title: Statistical Investigation of Chaotic Datastreams Using a Haar Wavelet Transform
Author(s): Carolyn Morgan*+ and Morris H. Morgan, III
Companies: Hampton University and Hampton University
Address: Queen and Tyler Streets, Hampton, VA, 23668, United States
Keywords: time series ; data stream ; nonparametric regression ; sampling
Abstract:

This paper will describe a statistical investigation of how wavelet methods are used to analyze datastreams arising from a chaotic source. Currently, extensive statistical research is being directed at devising detection algorithms that can distinguish between chaotic and random time series (signals) or detect subtle changes in a chaotic datastream. Such detection algorithms are used for image recognition, speech recognition, and diagnostic detection. These schemes have gained popularity because of the need to compress massive datastreams. The present research is focused on both data sampling strategies and data characterization. Digital datastreams are analyzed using the Haar wavelet, a subclass of orthogonal functions used for describing square integrable time series (signals). Wavelets can be used to accurately and parsimoniously represent other functions as well as for nonparametric regression applications. The focus of this work is to develop appropriate statistical metrics for such data streams and provide nonparametric estimates of population profiles of deterministic models that mimic random/stochastic processes.

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