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Asymptotic Properties of Generalized Cross Validation Estimators for Regularized System Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Chinese Acad Sci, Peoples R China.
Chinese Univ Hong Kong, Peoples R China.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2018 (English)In: 18th IFAC Symposium on System Identification (SYSID), Proceedings, ELSEVIER SCIENCE BV , 2018, Vol. 51, no 15, p. 203-208Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we study the asymptotic properties of the generalized cross validation (GCV) hyperparameter estimator and establish its connection with the Steins unbiased risk estimators (SURE) as well as the mean squared error (MSE). It is shown that as the number of data goes to infinity, the GCV has the same asymptotic property as the SURE does and both of them converge to the best hyperparameter in the MSE sense. We illustrate the efficacy of the result by Monte Carlo simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2018. Vol. 51, no 15, p. 203-208
Series
IFAC papers online, E-ISSN 2405-8963
Keywords [en]
Regularized system identification; Generalized cross-validation; Steins unbiased risk estimators; Asymptotic analysis
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-152411DOI: 10.1016/j.ifacol.2018.09.130ISI: 000446599200036OAI: oai:DiVA.org:liu-152411DiVA, id: diva2:1259592
Conference
18th IFAC Symposium on System Identification (SYSID)
Note

Funding Agencies|National Natural Science Foundation of China [61603379, 61773329]; National Key Basic Research Program of China (973 Program) [2014CB845301]; President Fund of Academy of Mathematics and Systems Science, CAS [2015-hwyxqnrc-mbq]; Thousand Youth Talents Plan - central government of China; Shenzhen Research Projects - Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Presidential Fund - Chinese University of Hong Kong, Shenzhen [PF.01.000249]; Swedish Research Council [2014-5894]

Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-30

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • Other style
More styles
Language
  • de-DE
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Output format
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