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Estimation of a covariance matrix with zeros

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  • Sanjay Chaudhuri
  • Mathias Drton
  • Thomas S. Richardson
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    Abstract

    We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call iterative conditional fitting, for computing the maximum likelihood estimate of the constrained covariance matrix, under the assumption of multivariate normality. In contrast to previous approaches, this algorithm has guaranteed convergence properties. Dropping the assumption of multivariate normality, we show how to estimate the covariance matrix in an empirical likelihood approach. These approaches are then compared via simulation and on an example of gene expression. Copyright 2007, Oxford University Press.

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    File URL: http://hdl.handle.net/10.1093/biomet/asm007
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    Bibliographic Info

    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 94 (2007)
    Issue (Month): 1 ()
    Pages: 199-216

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    Handle: RePEc:oup:biomet:v:94:y:2007:i:1:p:199-216

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    Cited by:
    1. Daniels, M.J. & Pourahmadi, M., 2009. "Modeling covariance matrices via partial autocorrelations," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2352-2363, November.
    2. Natalia Bailey & Sean Holly & N. Hashem Pesaran, 2013. "A Two Stage Approach to Spatiotemporal Analysis with Strong and weak cross Sectional Dependence," Cambridge Working Papers in Economics 1362, Faculty of Economics, University of Cambridge.
    3. Ohlson, Martin & von Rosen, Dietrich, 2010. "Explicit estimators of parameters in the Growth Curve model with linearly structured covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1284-1295, May.

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