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Estimation of the Cholesky decomposition in a conditional independent normal model with missing data

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  • He, Daojiang
  • Xu, Kai

Abstract

We investigate the problem of estimating the Cholesky decomposition in a conditional independent normal model with missing data. Explicit expressions for the maximum likelihood estimators and unbiased estimators are derived. By introducing a special group, we obtain the best equivariant estimators.

Suggested Citation

  • He, Daojiang & Xu, Kai, 2014. "Estimation of the Cholesky decomposition in a conditional independent normal model with missing data," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 27-39.
    • Handle: RePEc:eee:stapro:v:88:y:2014:i:c:p:27-39
    DOI: 10.1016/j.spl.2014.01.028
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    References listed on IDEAS

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    1. Sun, Xiaoqian & Sun, Dongchu, 2005. "Estimation of the Cholesky decomposition of the covariance matrix for a conditional independent normal model," Statistics & Probability Letters, Elsevier, vol. 73(1), pages 1-12, June.
    2. Dongchu Sun & Xiaoqian Sun, 2005. "Estimation of the multivariate normal precision and covariance matrices in a star-shape model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 455-484, September.
    3. Sun, Dongchu & Sun, Xiaoqian, 2006. "Estimation of multivariate normal covariance and precision matrices in a star-shape model with missing data," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 698-719, March.
    4. Ghosh M. & Sinha B. K., 1987. "Inadmissibility Of The Best Equivariant Estimators Of The Variance-Covariance Matrix, The Precision Matrix, And The Generalized Variance Under Entropy Loss," Statistics & Risk Modeling, De Gruyter, vol. 5(3-4), pages 201-228, April.
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