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11.
Ping Peng 《Statistics》2016,50(2):271-277
In this paper, we investigate the admissible minimax estimator (AME) of regression coefficient in Gauss–Markov model under a balanced loss function. In the class of homogeneous linear estimators, we obtain the AME under two occasions, respectively. We also prove that the AME is a shrinkage estimator of the best linear unbiased estimator (BLUE). Furthermore, we prove that the AME dominates the BLUE under certain conditions. 相似文献
12.
Tze Fen Li 《统计学通讯:理论与方法》2013,42(9):1029-1043
In an empirical Bayes decision problem, a simple class of estimators is constructed that dominate the James-Stein estimator, A prior distribution A is placed on a restricted (normal) class G of priors to produce a Bayes empirical Bayes estimator, The Bayes empirical Bayes estimator is smooth, admissible, and asymptotically optimal. For certain A rate of convergence to minimum Bayes risk is 0(n-1)uniformly on G. The results of a Monte Carlo study are presented to demonstrate the favorable risk bebhavior of the Bayes estimator In comparison with other competitors including the James-Stein estimator. 相似文献
13.
Let the p-component vector X be normally distributed with mean θ and covariance σ2I where I denotes the identity matrix. Stein's estimator of θ is kown to dominate the usual estimator X for p ≥ 3, We obtain a family of estimators which dominate Stein's estimator for p≥ 3 相似文献
14.
This paper considers the generalised empirical Bayes two-action (testing) and multiple action problems concerning a distribution function The Dirichiet process priors p of Ferguson have been used as the prior distributions on the space of distribution functions on the real line. The two-action component problem is considered in detail and when p is unknown partially empirical Bayes procedures {6 } which are asymptotically optimal with rates 0{jT1/2) and OCCmCn+l)) 相似文献
15.
Simultaneous estimation of p gamma scale-parameters is considered under squared-error loss. The problem of minimizing, subject to uniform risk domination, the Bayes risk (or more generally the posterior expected loss) against certain conjugate or mixtures of conjugate priors is considered. Rather surprisingly, it is shown that the minimization can be done conditionally, thus avoiding variational arguments. Relative savings loss (and a posterior version thereof) are found, and it is found that in the most favorable situations, Bayesian robustness can be achieved without sacrificing substantial subjective Bayesian gains. 相似文献
16.
In this study, the necessary and sufficient conditions for the Liu-type (LT) biased estimator are determined. A test for choosing between the LT estimator and least-squares estimator is obtained by using these necessary and sufficient conditions. Also, a simulation study is carried out to compare this estimator against the ridge estimator. Furthermore, a numerical example is given for defined test statistic. 相似文献
17.
In this article we have presented some of the asymptotic theorems related to one-truncation parameter family of distributions ? Comparison of performance of different estimators and other inferential problems have been tackled - Also applications of the main results have been given and illustrated their uses with examples. 相似文献
18.
Inga Samonenko 《Journal of nonparametric statistics》2017,29(3):515-530
We consider permutation tests based on a likelihood ratio like statistic for the one way or k sample design used in an example in Kolassa and Robinson [(2011), ‘Saddlepoint Approximations for Likelihood Ratio Like Statistics with Applications to Permutation Tests’, Annals of Statistics, 39, 3357–3368]. We give explicitly the region in which the statistic exists, obtaining results which permit calculation of the statistic on the boundary of this region. Numerical examples are given to illustrate improvement in the power of the tests compared to the classical statistics for long-tailed error distributions and no loss of power for normal error distributions. 相似文献
19.
Jan F. Bj⊘rnstad 《统计学通讯:理论与方法》2013,42(23):2411-2433
The multiple decision problem of selecting a random non-empty subset of populations, out of k populations, that are close in some sense to the best population is considered in a decision-theoretic framework. Uniformly optimal procedures for non-negative semi-additive loss are derived. A class of likelihood-ratio type of procedures is shown to be admissible for monotone additive loss. 相似文献
20.
Bechhofer and Tamhane (1981) proposed a new class of incomplete block designs called BTIB designs for comparing p ≥ 2 test treatments with a control treatment in blocks of equal size k < p + 1. All BTIB designs for given (p,k) can be constructed by forming unions of replications of a set of elementary BTIB designs called generator designs for that (p,k). In general, there are many generator designs for given (p,k) but only a small subset (called the minimal complete set) of these suffices to obtain all admissible BTIB designs (except possibly any equivalent ones). Determination of the minimal complete set of generator designs for given (p,k) was stated as an open problem in Bechhofer and Tamhane (1981). In this paper we solve this problem for k = 3. More specifically, we give the minimal complete sets of generator designs for k = 3, p = 3(1)10; the relevant proofs are given only for the cases p = 3(1)6. Some additional combinatorial results concerning BTIB designs are also given. 相似文献