Kolumbán, Sándor and Csáji, Balázs Csanád (2018) Towards DOptimal Input Design for FiniteSample System Identification. In: 18th IFAC Symposium on System Identification, July 911, 2018, Stockholm, Sweden.

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Abstract
Finitesample system identification methods provide statistical inference, typically in the form of confidence regions, with rigorous nonasymptotic guarantees under minimal distributional assumptions. Data Perturbation (DP) methods constitute an important class of such algorithms, which includes, for example, SignPerturbed Sums (SPS) as a special case. Here we study a natural input design problem for DP methods in linear regression models, where we want to select the regressors in a way that the expected volume of the resulting confidence regions are minimized. We suggest a general approach to this problem and analyze it for the fundamental building blocks of all DP confidence regions, namely, for ellipsoids having confidence probability exactly 1/2. We also present experiments supporting that minimizing the expected volumes of such ellipsoids significantly reduces the average sizes of the constructed DP confidence regions.
Item Type:  Conference or Workshop Item (Paper) 

Subjects:  Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány T Technology / alkalmazott, műszaki tudományok > TK Electrical engineering. Electronics Nuclear engineering / elektrotechnika, elektronika, atomtechnika 
Depositing User:  Dr. Balázs Csanád Csáji 
Date Deposited:  05 Oct 2018 07:12 
Last Modified:  05 Oct 2018 07:12 
URI:  http://real.mtak.hu/id/eprint/86589 
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