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Forward adaptive banding for estimating large covariance matrices

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  • Chenlei Leng
  • Bo Li

Abstract

We propose a simple forward adaptive banding method for estimating large covariance matrices using the modified Cholesky decomposition. This approach requires the fitting of a prespecified set of models due to the adaptive banding structure and can be efficiently implemented. Aside from its computational attractiveness, we propose a novel Bayes information criterion that gives consistent model selection for estimating high dimensional covariance matrices. The method compares favourably to its competitors in simulation study. Copyright 2011, Oxford University Press.

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  • Chenlei Leng & Bo Li, 2011. "Forward adaptive banding for estimating large covariance matrices," Biometrika, Biometrika Trust, vol. 98(4), pages 821-830.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:4:p:821-830
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    File URL: http://hdl.handle.net/10.1093/biomet/asr045
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    Cited by:

    1. Lopes, Hedibert F. & McCulloch, Robert E. & Tsay, Ruey S., 2022. "Parsimony inducing priors for large scale state–space models," Journal of Econometrics, Elsevier, vol. 230(1), pages 39-61.

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