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Estimation of multivariate normal covariance and precision matrices in a star-shape model with missing data

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  • Sun, Dongchu
  • Sun, Xiaoqian

Abstract

In this paper, we study the problem of estimating the covariance matrix [Sigma] and the precision matrix [Omega] (the inverse of the covariance matrix) in a star-shape model with missing data. By considering a type of Cholesky decomposition of the precision matrix [Omega]=[Psi]'[Psi], where [Psi] is a lower triangular matrix with positive diagonal elements, we get the MLEs of the covariance matrix and precision matrix and prove that both of them are biased. Based on the MLEs, unbiased estimators of the covariance matrix and precision matrix are obtained. A special group , which is a subgroup of the group consisting all lower triangular matrices, is introduced. By choosing the left invariant Haar measure on as a prior, we obtain the closed forms of the best equivariant estimates of [Omega] under any of the Stein loss, the entropy loss, and the symmetric loss. Consequently, the MLE of the precision matrix (covariance matrix) is inadmissible under any of the above three loss functions. Some simulation results are given for illustration.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:97:y:2006:i:3:p:698-719
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    1. Tatsuya Kubokawa & Yoshihiko Konno, 1990. "Estimating the covariance matrix and the generalized variance under a symmetric loss," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(2), pages 331-343, June.
    2. Konno, Y., 1995. "Estimation of a Normal Covariance Matrix with Incomplete Data under Stein's Loss," Journal of Multivariate Analysis, Elsevier, vol. 52(2), pages 308-324, February.
    3. Liu, Chuanhai, 1999. "Efficient ML Estimation of the Multivariate Normal Distribution from Incomplete Data," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 206-217, May.
    4. 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.
    5. Konno, Yoshihiko, 2001. "Inadmissibility of the Maximum Likekihood Estimator of Normal Covariance Matrices with the Lattice Conditional Independence," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 33-51, October.
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    1. Withers, Christopher S. & Nadarajah, Saralees, 2011. "Estimates of low bias for the multivariate normal," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1635-1647, November.
    2. 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.

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