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渐近最优经验bayes估计
相关语句
  asymptotic optimal empirical bayes estimator
     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
短句来源
     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.
短句来源
  asymptotically optimal empirical bayes estimators
     ASYMPTOTICALLY OPTIMAL EMPIRICAL BAYES ESTIMATORS OF CONTINUOUS FUNCTIONS OF THE PARAMETER IN ONE DIMENSIONAL DISCRETE EXPONENTIAL FAMILY
     一维离散指数族参数的连续函数的渐近最优经验Bayes估计
短句来源
     THE ASYMPTOTICALLY OPTIMAL EMPIRICAL BAYES ESTIMATORS OF VARIANCE COMPONENTS IN ONE-WAY CLASSIFICATION RANDOM EFFECT MODEL
     单向分类随机效应模型中方差分量的渐近最优经验Bayes估计
短句来源
  “渐近最优经验bayes估计”译为未确定词的双语例句
     The Asymptotically Optimal(a,o)Empirical Bayes(EB)Estimation about a Class of Discrete Distribution Families
     一类离散分布参数的渐近最优经验Bayes估计
短句来源
     Asympototically Optimal Empirical Bayes Estimatrs Measureble Functions of the Parameter in One Dimensional Discrete Exponential Family
     离散指数族参数的可测函数渐近最优经验Bayes估计
短句来源
     Asymptotically Optimal Empirical Bayes Estimation for Parameter-functions Of Two-sided Truncation Distribution Families
     双边截断型分布族参数函数的渐近最优经验Bayes估计
短句来源
     n this paper,the empirical Bayes estimate of the parameter of uniform distribution family is introduced and for the absolute error loss its asymptotically optimal property is proved with respect to the prior distribution family(1).
     在均匀分布场合,文献“均匀分布族参数在绝对误差损失下的渐近最优经验Bayes估计”构造了一类绝对值损失下的经验Bayes估计,并证明了它的渐近最优性,在类似于文献“均匀分布族参数在绝对误差损失下的渐近最优经验Bayes估计”的条件下,构造了一类与其不同的经验Bayes估计,并用与其不同的方法证明了估计的渐近最优性。
短句来源
     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
短句来源
更多       
  相似匹配句对
     Empirical Bayes Estimation
     经验Bayes估计
短句来源
     Asymptotic Property of Linear Empirical Bayes Estimation
     线性经验Bayes估计渐近性质
短句来源
     The Asymptotically Optimal(a,o)Empirical Bayes(EB)Estimation about a Class of Discrete Distribution Families
     一类离散分布参数的渐近最优经验Bayes估计
短句来源
     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
短句来源
     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
短句来源
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Censider a family of discrete exponential distribution in the form. p(X=x) =h(x)β(θ)θ~x x=0, 1, 2,… (1) θ∈Θ, Θ={θ:θ>0, sum from x=0 to ∞(h(x)θ~x<∞} Put f(θ)be a measureble function under some conditions andF={G: integral from Θto | f(θ)| ~2dG(θ)<∞} be a class of prior distrubution on Θ We obtain following results Theorem 1 Let h(x)>0 x=0, 1, 2,… (2) The lost function L(θ, δ)=(f(θ)-δ)~2, then an asymptotically optimal empirical Bayes(a.oEB) estimator of f(θ)relative to the class F of prior distributions...

Censider a family of discrete exponential distribution in the form. p(X=x) =h(x)β(θ)θ~x x=0, 1, 2,… (1) θ∈Θ, Θ={θ:θ>0, sum from x=0 to ∞(h(x)θ~x<∞} Put f(θ)be a measureble function under some conditions andF={G: integral from Θto | f(θ)| ~2dG(θ)<∞} be a class of prior distrubution on Θ We obtain following results Theorem 1 Let h(x)>0 x=0, 1, 2,… (2) The lost function L(θ, δ)=(f(θ)-δ)~2, then an asymptotically optimal empirical Bayes(a.oEB) estimator of f(θ)relative to the class F of prior distributions can be constructed. We also obtain sufficient condition which there exist a.O EB eslimatoys concenjng measureble function of the parameter of distribution family.

考虑离散型指数分布族 P_θ(X=x)=h(x)β(θ)θ~x x=0,1,2,…θ∈Θ,Θ={θ: θ>0,sum from x=0 to ∞ h(x)θ~x<∞} F={G: integral fromΘ | f(θ)|~2dG(θ)<∞}是在参数空间Θ上的一个先验分布族,其中f(θ)是一待估的可测函数。本文在一些条件下证明在平方损失函数L(θ,δ)=(f(θ)-δ)~2下,相对于先验族F,可以构造出f(θ)的渐近最优经验Bayes估计

AbstractThe empirical Bayes(EB)estimation problem for multinomial distribution is considered.By using a special sample structure,we propose the EB estimators of the parameter vector.These estimators is proved to be asymptotically optimal with convergence rates O(n-1) for any prior distributions.

本文考虑了多项分布参数的经验Bayes估计问题。通过利用一种较特殊的样本结构,我们构造了参数向量的渐近最优的经验Bayes估计,并证明了对于参数向量的任何先验分布,该估计的收敛速度为O(n-1)。

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.

 
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