Double shrinkage estimators in the GMANOVA model

Journal of Multivariate Analysis

Volumn 56, Issue 2, 1996, Pages 245-258

  Kariya, Takeaki ^a   Konno, Yoshihiko ^b   Strawderman, William E ^c  

^a HITOTSUBASHI UNIVERSITY   (Japan)

^b CHIBA UNIVERSITY   (Japan)

^c NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0030076591     PISSN: 0047259X     EISSN: None     Source Type: Journal    
DOI: 10.1006/jmva.1996.0013     Document Type: Article

References (12)

1. [1] BILODEAU, M., AND KARIYA, T. (1989). Minimax estimators in the normal MANOVA model. J. Multivariate Anal. 28 260-270.
2. [2] GLESER, L. (1986). Minimax estimators of a normal mean vector of a normal distribution with unknown covariance matrix. Ann. Statist. 14 1625-1633.
3. [3] HAFF, L. R. (1976). An identity for the Wishart distribution with applications. J. Multivariate Anal. 9 531-544.
4. [4] HAFF, L. R. (1976). Estimation of the inverse covariance matrix: Random mixtures of the inverse Wishart matrix and identity. Ann. Statist. 6 1264-1276.
5. [5] KARIYA, T. (1989). Equivariant estimation in a model with an ancillary statistic. Ann. Statist. 17 920-928.
6. [6] KONNO, Y. (1991). On estimation of a matrix of normal means with unknown covariance matrix. J. Multivariate Anal. 36 44-55.
7. [7] KUBOKAWA, T., SALEH, A. K. MD. E., AND MORITA, K. (1992). Improving on MLE of coefficient matrix in a growth curve model. J. Statist. Planning and Infer. 31 169-177.
8. [8] MUIRHEAD, R. J. (1982). Aspects of Multivariate Statistical Theory. Wiley, New York.
9. [9] POTTHOFF, R. F., AND ROY, S. N. (1964). A generalized multivariate analysis of variance model useful especially for growth curve problems. Biometrika 51 313-326.
10. [10] RAO, C. R. (1967). Least squares theory using an estimated dispersion matrix and its application to measurement of signals. In Fifth Berkeley Symposium on Math. Statistics and Probability, Vol. 1, pp. 355-372. Univ. of California, Berkeley.
11. [11] STRAWDERMAN, W. E. (1973). Proper Bayes minimax estimators of the multivariate normal mean vector for the case of common unknown variances. Ann. Statist. 1 1189-1194.
12. [12] TAN, M. (1991). Improved estimators for the GMANOVA problem with application to Monte Carlo Simulation. J. Multivariate Anal. 38 262-274.