1 |
BREWSTER, J. F. AND ZIDEK, J. V. (1974). 'Improving on equivariant estimators', The Annals of Statistics, 2, 21-38
DOI
|
2 |
GHOSH, M. (1994). 'On some Bayesian solutions of the Neyman-Scott problem', In Statistical Decision Theory and Related Topics V (S. S. Gupta and J. O. Berger. eds.), 267-276, Springer, New York
|
3 |
PROSKIN, H. M. (1985). 'An admissibility theorem with applications to the estimation of the variance of the normal distribution', Ph. D. Dissertation, Department of Statistics, Rutgers University, New Jersey
|
4 |
STRAWDERMAN, W. E. (1974). 'Minimax estimation of powers of the variance of a normal population under squared error loss', The Annal of Statistics, 2, 190-198
DOI
|
5 |
Box, G. E. P. AND TIAO, G. C. (1973). Bayesian Inference in Statistical Analysis, Addison-Wesley, Massachusetts
|
6 |
PORTNOY, S. (1971). 'Formal Bayes estimation with application to a random effects model', The Annals of Mathematical Statistics, 42, 1379-1402
DOI
|
7 |
DATTA, G. S. AND GHOSH, M. (1995). 'Hierarchical Bayes estimators of the error variance in one-way ANOVA models', Journal of Statistical Planning and Inference, 45, 399-411
DOI
ScienceOn
|
8 |
BROWN, L. (1968). 'Inadmissiblity of the usual estimators of scale parameters in problems with unknown location and scale parameters', Annals of Mathematical Statistics, 39, 29-48
DOI
|
9 |
DONG, K. H. AND KIM, B. H. (1993). 'Sufficient conditions for the admissibility of estimators in the multiparameter exponential family', Journal of the Korean Statistical Society, 22, 55-69
|
10 |
BERGER, J. O. AND BERNARDO, J. M. (1992). 'On the development of reference priors', Bayesian Statistics 4 (J. M. Bernardo, et al. eds.), 35-60, Oxford University Press, New York
|
11 |
STEIN, C. (1964). 'Inadmissibility of the usual estimator of the variance of a normal distribution with unknown mean', Annals of the Institute of Statistical Mathematics, 16, 155-160
DOI
|
12 |
MAATTA, J. M AND CASELLA, G. (1990). 'Developments in decision theoretic variance estimation (with discussion)', Statistical Science, 5, 90-120
DOI
|