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Joint likelihood estimation and model order selection for outlier censoring

Karbasi, S. M ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1049/rsn2.12072
  3. Publisher: John Wiley and Sons Inc , 2021
  4. Abstract:
  5. This study deals with the problem of outlier censoring from the secondary data in a radar scenario, where the number of outliers is unknown. To this end, a procedure consisting of joint likelihood estimation and statistical model order selection (MOS) is proposed. Since the maximum likelihood (ML) estimation of the outlier subset requires to solve a combinatorial problem, an approximate ML (AML) method is employed to reduce the complexity. Therefore, to determine the number of outliers, different MOS criteria based on likelihood function are applied. At the analysis stage, the performance of the proposed methods is assessed based on simulated data. The results highlight that the devised algorithms exhibit satisfactory performance with efficient complexity at the same time. © 2021 The Authors. IET Radar, Sonar & Navigation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
  6. Keywords:
  7. Statistics ; Combinatorial problem ; Joint likelihoods ; Likelihood functions ; Model-order selection ; Secondary datum ; Statistical modeling ; Maximum likelihood estimation
  8. Source: IET Radar, Sonar and Navigation ; Volume 15, Issue 6 , 2021 , Pages 561-573 ; 17518784 (ISSN)
  9. URL: https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/rsn2.12072