Optimal experiment design in a filtering context with application to sampled network data
| Autor: | Harsh Singhal, George Michailidis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Optimal design network monitoring Mathematical optimization Steady state (electronics) Optimization problem Computer science Design of experiments Context (language use) random walks Kalman filter Flow network Statistics - Applications Modeling and Simulation State space Applications (stat.AP) Statistics Probability and Uncertainty |
| Zdroj: | Ann. Appl. Stat. 4, no. 1 (2010), 78-93 |
| Popis: | 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. Published in at http://dx.doi.org/10.1214/09-AOAS283 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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