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- W2969027719 startingPage "106815" @default.
- W2969027719 abstract "Estimating covariance matrices is an important research topic in statistics and finance. A semiparametric model for covariance matrix estimation is proposed. Specifically, the covariance matrix is modeled as a polynomial function of the symmetric adjacency matrix with time varying parameters. The asymptotic properties for the time varying coefficient and the associated semiparametric covariance estimators are established. A Bayesian information criterion to select the order of the polynomial function is also investigated. Simulation studies and an empirical example are presented to illustrate the usefulness of the proposed method." @default.
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- W2969027719 date "2020-02-01" @default.
- W2969027719 modified "2023-10-15" @default.
- W2969027719 title "Semiparametric model for covariance regression analysis" @default.
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- W2969027719 doi "https://doi.org/10.1016/j.csda.2019.106815" @default.
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