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March 2010 Optimal experiment design in a filtering context with application to sampled network data
Harsh Singhal, George Michailidis
Ann. Appl. Stat. 4(1): 78-93 (March 2010). DOI: 10.1214/09-AOAS283

Abstract

We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, we relate our work to the general problem of steady state optimal design for state space models.

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Harsh Singhal. George Michailidis. "Optimal experiment design in a filtering context with application to sampled network data." Ann. Appl. Stat. 4 (1) 78 - 93, March 2010. https://doi.org/10.1214/09-AOAS283

Information

Published: March 2010
First available in Project Euclid: 11 May 2010

zbMATH: 1189.62123
MathSciNet: MR2758085
Digital Object Identifier: 10.1214/09-AOAS283

Keywords: Kalman filter , network monitoring , optimal design , Random walks

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 1 • March 2010
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