DOI QR코드

DOI QR Code

James-Stein Type Estimators Shrinking towards Projection Vector When the Norm is Restricted to an Interval

  • Baek, Hoh Yoo (Division of Mathematics and Informational Statistics, Wonkwang University) ;
  • Park, Su Hyang (Department of Informational Statistics, Graduate School, Wonkwang University)
  • 투고 : 2017.02.06
  • 심사 : 2017.03.25
  • 발행 : 2017.03.30

초록

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p-q{\geq}3)$, $q=rank(P_V)$ with a projection matrix $P_v$ under the quadratic loss, based on a sample $X_1$, $X_2$, ${\cdots}$, $X_n$. We find a James-Stein type decision rule which shrinks towards projection vector when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}{\theta}-P_V{\theta}{\parallel}$ is restricted to a known interval, where $P_V$ is an idempotent and projection matrix and rank $(P_V)=q$. In this case, we characterize a minimal complete class within the class of James-Stein type decision rules. We also characterize the subclass of James-Stein type decision rules that dominate the sample mean.

키워드

참고문헌

  1. W. James and D. Stein, "Estimation with guadratic loss", In Proceedings Fourth Berkeley Symposium on Mathematical statistics and Probability, California University Press, Berkeley, pp. 361-380 1961.
  2. C. M. Stein, "Confidence sets for the mean of a multivariate normal distribution", J. R. Stat. soc. B., Vol. 24, pp. 265-296, 1962.
  3. W. E. Strawderman, "Minimax estimation of location parameters for certain spherically symmetric distributions", J. Multivariate Anal., Vol. 4, pp, 255-264, 1974. https://doi.org/10.1016/0047-259X(74)90032-3
  4. S.-I. Amari, "Differential geometry of curved exponential families-curvature and information loss", Ann. Stat., Vol. 10, pp. 357-385, 1982. https://doi.org/10.1214/aos/1176345779
  5. T. Kariya, "Equivariant estimation in a model with an ancillary statistic", Ann. Stat., Vol. 17, pp. 920-928, 1989. https://doi.org/10.1214/aos/1176347151
  6. F. Perron and N. Giri, "On the best equivariant estimator of mean of a multivariate normal population", J. Multivariate Anal., Vol. 32, pp. 1-16, 1989.
  7. E. Marchand and N. C. Giri, "James-stein estimation with constraints on the norm", Commun. Stat.-Theor. M., Vol. 22, pp. 2903-2924, 1993. https://doi.org/10.1080/03610929308831192
  8. H. Y. Baek, "Lindley type estimators with the known norm", Journal of the Korean Data and Information Science Society, Vol. 11, pp. 37-45, 2000.
  9. J. Berger, "Minimax estimation of location vectors for a wide class of densities", Ann. Stat., Vol. 3, pp. 1318-1328, 1975. https://doi.org/10.1214/aos/1176343287
  10. S. C. Chow and S. C. Wang, "A note an adaptive generalized ridge regression estimator", Statistics and Probability Letters, Vol. 10, pp. 17-21, 1990. https://doi.org/10.1016/0167-7152(90)90106-H
  11. M. F. Egerton and P. J. Laycock, "An explicit formula for the risk of James-Stein estimators", Can. J. Stat., Vol. 10, pp. 199-205, 1982. https://doi.org/10.2307/3556182
  12. G. Bravo and G. MacGibbon, "Improved shrinkage estimators for the mean of a scale mixture of normals with unknown variance", Can. J. Stat., Vol. 16, pp. 237-245, 1988. https://doi.org/10.2307/3314730