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Abstract Details

Activity Number: 327
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract - #304589
Title: Adaptive Sampling of Discrete Time Locally Stationary Processes
Author(s): Matthew Nunes*+ and Idris Eckley
Companies: Lancaster University and Lancaster University
Address: Fylde College, Lancaster, _, LA1 4YF, United Kingdom
Keywords: locally stationary processes ; time series ; aliasing ; discrete time

Scientific experiments often measure real-world processes which are nonstationary in nature, i.e. the covariance structure change over time. In many cases, data is collected at high sampling rates, producing large amounts of data for analysis. One way of dealing with this high data storage need is to subsample the data appropriately. If too aggressive a subsampling regime is used, it will introduce "aliasing" in the signal, where high frequency signal components are not captured, leading to signal confounding. In this talk, we discuss sampling regimes based on the recently proposed alias detection procedure of Eckley and Nason (2011).

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