liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Asymptotic Properties of Hyperparameter Estimators by Using Cross-Validations 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; Chinese Univ Hong Kong, Peoples R China.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2018 (English)In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 644-649Conference paper, Published paper (Refereed)
Abstract [en]

This paper studies the asymptotic properties of the hyperparameter estimators including the leave-k-out cross validation (LKOCV) and r-fold cross validation (RFCV), and discloses their relation 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 LKOCV shares the same asymptotic best hyperparameter minimizing the MSE estimator as the SURE does if the input is bounded and the ratio between the training data and the whole data tends to zero. We illustrate the efficacy of the theoretical result by Monte Carlo simulations.

Place, publisher, year, edition, pages
IEEE , 2018. p. 644-649
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords [en]
Regularized system identification; Linear system identification; Hyperparameter Estimators; Leave-k-out cross validation; r-fold cross validation; Asymptotic analysis
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-154759DOI: 10.1109/CDC.2018.8618682ISI: 000458114800090ISBN: 978-1-5386-1395-5 (print)OAI: oai:DiVA.org:liu-154759DiVA, id: diva2:1291935
Conference
57th IEEE Conference on Decision and Control (CDC)
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]; Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Chinese University of Hong Kong, Shenzhen [2014.0003.23]; Swedish Research Council [2014-5894]; Thousand Youth Talents Plan - central government of China; [PF. 01.000249]

Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2019-02-26

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Mu, BiqiangLjung, Lennart
By organisation
Automatic ControlFaculty of Science & Engineering
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 18 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf