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empirical bayes estimator
    A QUADRATIC EMPIRICAL BAYES ESTIMATOR OF CORRELATION COEFFICIENT OF TWO NORMAL DISTRIBUTIONS
    正态分布相关系数的二次经验Bayes估计
    An Empirical Bayes Estimator of Variance Components in One-Way Random Model
    单向分类随机模型方差分量的经验Bayes估计
    The Convergence Rate of Simultaneous Empirical Bayes Estimator for Parameters in Linear Regression Models
    线性回归模型参数的联立经验Bayes估计的收敛速度
    The Uniform Convergence Rate of Multi—Parameter Linear Empirical Bayes Estimator
    多维参数线性经验 Bayes 估计的一致收敛速度
    CONVERGENCE RATE OF EMPIRICAL BAYES ESTIMATOR IN A LINEAR MODEL
    线性模型中经验Bayes估计的收敛速度
    A Linear Empirical Bayes Estimator of Parameter Vector in Linear Regression Model
    线性模型中参数向量的线性经验Bayes估计
    Empirical Bayes Estimator of Reliability under Type-I Life Test
    定时截尾试验下可靠性的经验Bayes估计
    Secondly, under the square loss, we construct the asymptotically optimal and admissible empirical Bayes estimator of the mean for normal distribution, theconvergence rate O(n~(-1)) is also obtained.
    其次,在平方损失下构造了正态分布均值的渐近最优和可容许的经验Bayes估计,并得到了其收敛速度为O(n~(-1))。
    CONVERGENCE RATE OF THE ASYMPTOTIC OPTIMAL EMPIRICAL BAYES ESTIMATOR OF THE PARAMETERS IN NORMAL DISTRIBUTION
    正态分布参数的渐近最优经验Bayes估计的收敛速度
    ASYMPTOTICALLY OPTIMAL EMPIRICAL BAYES ESTIMATOR OF THE PARAMETER IN A UNIFORM DISTRIBUTION FAMILY UNDER THE ABSOLUTE LOSS FUNCTION
    一类均匀分布在绝对值损失下的渐近最优经验Bayes估计
    THE BEST UNIFORM CONVERGENCE RATE OF MULTI-PARAMETER LINEAR EMPIRICAL BAYES ESTIMATOR
    多维参数线性经验Bayes估计一致收敛速度的最优性
    Based on these expansions, the empirical Bayes estimator is shown to have smaller mean squared error than the customary variance estimator, i. e., the within group sample variance, if N is large.
    基于这些展开式,在N很大时证明了经验Bayes估计要比常用方差估计(组内样本方差)有更小的均方误差。
    Under suitable conditions we obtained precise asymptotic behavior of linear empirical Bayes estimator and its conditional Bayes risk.
    在适当条件下,证得了线性经验Bayes估计和它的条件Bayes风险的精确渐近结构。
    For the square loss function, an empirical Bayes estimator of the model parameters is constructed by the estimation of kernel density and its partial derivatives. The estimator is nonnegative.
    本文采用经验Bayes方法,在平方损失下,利用密度函数及其偏导数的核估计构造出方差分量的一个经验Bayes估计,这个估计是非负的。
    In this paper we construct the empirical Bayes estimator of regression coefficient in multiple linear model, by using the kernel estimator method of multiple density function and its partial derivative. We obtain the uniform convergence rate of the estimator.
    利用多元密度函数及其导数的核估计方法,建立了多元线性模型回归系数的经验Bayes估计,并给出了这种估计的一致收敛速度.
    The empirical Bayes estimator of parameter θ in a linear model Y=θ+εis studied. Under some trivial restructions on the continuous differentiability,the asymptotic optimal empirical Bayes estimator of the parameter θ is constructed,and the convergence rate of this estimator is also given.
    考虑了线性模型Y=θ+ε中参数θ的经验Bayes估计问题,在对ε的密度函数的连续可微性给以较一般限制的条件下,构造了θ的渐近最优经验Bayes估计并且给出了这个估计的收敛速度.
    In this paper,a linear empirical Bayes estimator of unknown parameter vector is constructed in linear regression model. Under some conditions, its asymptotic optimal convergence is proveded with convergent rate O (). Song's[8] results are improved esentially.
    本文利用线性经验Bayes估计的思想,对线性回归模型中未知参数向量构造了一类线性经验Bayes估计,并在一定条件下证明了其具有的a.o.收敛速度.
    In this paper, it is proved that there exists no multi-parameter liner empirical Bayes estimator with uniform convergence rate larger than one. So, the uniform convergence rate of linear empirical bayes estimator constructed in [1] is shown to be the best.
    证明出任何一个多维参数线性经验Bayes估计的一致收敛速度不可能超过1,从而说明文中构造的线性经验Bayes估计的一致收敛速度1是最优的。
    The purpose of this paper is to give the empirical Bayes estimator of scale-parameter for the two-parameter exponential distribution under the type-Ⅱ censoring life test with indirect empirical Bayes method.
    对于定数截尾试验下双参数指数分布刻度参数的经验 Bayes估计问题 ,用间接 EB方法得到其 EB估计 .
    In this paper,the empirical Bayes estimator of reliability under type I life test is discussed Approximate interval estimation and EB estimator of failure rate λ is given.
    讨论定时截尾试验下可靠性的经验Bayes估计 ,给出了失效率λ的EB估计和近似区间估计。
 

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