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    Robust Estimation of Scatter Matrix, Random Matrix Theory and an Application to Spectrum Sensing

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    Type
    Thesis
    Authors
    Liu, Zhedong cc
    Advisors
    Alouini, Mohamed-Slim cc
    Committee members
    Rue, Haavard cc
    Kammoun, Abla cc
    Program
    Statistics
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-05-05
    Permanent link to this record
    http://hdl.handle.net/10754/652444
    
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    Abstract
    The covariance estimation is one of the most critical tasks in multivariate statistical analysis. In many applications, reliable estimation of the covariance matrix, or scatter matrix in general, is required. The performance of the classical maximum likelihood method relies a great deal on the validity of the model assumption. Since the assumptions are often approximately correct, many robust statistical methods have been proposed to be robust against the deviation from the model assumptions. M-estimator is an important class of robust estimator of the scatter matrix. The properties of these robust estimators under high dimensional setting, which means the number of dimensions has the same order of magnitude as the number of observations, is desirable. To study these, random matrix theory is a very important tool. With high dimensional properties of robust estimators, we introduced a new method for blind spectrum sensing in cognitive radio networks.
    Citation
    Liu, Z. (2019). Robust Estimation of Scatter Matrix, Random Matrix Theory and an Application to Spectrum Sensing. KAUST Research Repository. https://doi.org/10.25781/KAUST-42166
    DOI
    10.25781/KAUST-42166
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-42166
    Scopus Count
    Collections
    Theses; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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