一种对称损失函数下一类指数分布族刻度参数的估计

在p,q对称熵损失函数L(θ,δ)=θp/δp+δq/θq-2(p,q0)下,研究了一类指数分布族c(x,n)θ-ve-T(x)/θ的刻度参数θ的Bayes估计与可容许估计,并应用积分变换定理证明了这两个估计具有不变性.     

熵损失函数下两参数Lomax分布形状参数的Bayes估计

在熵损失函数下,讨论了两参数Lomax分布形状参数的Bayes估计和可容许估计.并讨论了一类(cT+d)~(-1)形式估计的可容许性和不可容许性.     

一种对称损失函数下正态总体刻度参数的估计

本文研究正态分布中刻度参数在损失函数L(σ，δ)=[(σ-δ)^2]/σδ下的最小风险同变估计及Bayes估计，并讨论(cT(x) d)^1/2形式估计的可容许性与不可容许性，我们发现在这种损失下σ的极大似然估计是不可容许的．     

Mlinex损失函数下一类分布族参数的Bayes估计

考虑分布函数形如F(x;θ)=1-[g(x)]~θ或[1—g(x)]~θ,A≤x≤B,θ0的分布族,其中g(x)是关于x单调递减的可微函数,且g(A)=1,g(B)=0.在Mlinex损失函数下,给出了其中参数θ的Bayes估计及其容许性,并对分布的一个充分统计量的逆线性形式的容许性进行讨论.最后通过蒙特卡洛模拟说明Bayes估计在小样本情形时的优良表现.     

多维正态总体均值θ的一类广义Bayes minimax估计

本文给出了均值为θ,协方差矩阵为∑(∑正定未知)的P(P≥3)维正态总体均值θ在损失L(δ(x),θ)=((x)-θ)’∑~(-1)((x)-θ)之下的一类广义的Bayes minimax估计,推广了Berger[2]的结果。     

在熵损失函数下定数截尾情形的参数估计——指数分布情形

本文研究了在熵损失函数下，定数截尾时指数分布的参数估计，得出在熵损失函数下的最小风险同变（ＭＲＥ）估计的精确形式．证明了（ｃＴ＋ｄ）~(－１)形式的一类估计的可容许性和不可容许性．     

单参数指数型分布族中的可容许估计

§1 引言1958年,Karlin 在平方损失下,对于单参数指数型分布族 p(x,θ)-β(θ)·e~,得到了ax 是 E_θx=-β′(θ)/β(θ)的可容许估计的充分条件,即众所周知的 Karlin 定理.并且[1]对于两种类型的截断型分布族 p(x,θ)=q(θ)·r(x),b>x>θ和 p(x,θ)=q(θ)·r(x),a<α<θ,证明了(2a+1)/(a+1)·q~(-a)(x)是 q~(-a)(θ)(0<α<∞,已知)的可容许估计.1961年,Katz 对单参数指数型分布族,讨论了限制参数空间的可容许估计问题.1964年,成平应用 Cramr-Rao 不等式,把 Karlin 定理推广到更为一般的情况.1977年,Ghosh 和 Meeden 及1981年,     

指数分布族参数的渐近最优与可容许的经验Bayes估计的再探讨

文献[1]在平方损失及超参数α服从伽玛分布且X1′,…Xn′(历史样本)和X(当前样本)独立同分布的条件下,构造了指数分布族{f(x|λ)=λe-λx,λ>0,x>0}的参数λ的渐近最优与可容许的经验Bayes估计.本文在超参数α分别服从伽与分布与指数分布且当前样本由X扩充为X1,…,Xm的情况下,重新构造了指数分布族参数λ的渐进最优与可容许的经验Bayes估计,从而将文献[1]的结果进行了推广.     

不同损失函数下Pareto分布参数的Bayes估计

首先给出了Pareto分布参数的极大似然估计;其次在对称损失,二次损失,Mlinex损失函数下,给出了参数的Bayes估计,并证明了所给估计都是容许的;最后通过实例,对所给的几个估计的优良性进行了分析,结果表明在Mlinex损失下,参数θ的Bayes估计值更接近真实值     

双边截断型分布族参数函数的渐近最优经验Bayes估计

本文考虑一维双边截断型分布族参数函数在平方损失下的经验 Bayes估计问题 .给定θ,X的条件分布为f (x|θ) =ω(θ1,θ2 ) h(x) I[θ1,θ2 ] (x) dx其中θ =(θ1,θ2 )T(x) =(t1(x) ,t2 (x) ) =(min(x1,… ,xm) ,max(x1,… ,xm) )是充分统计量 ,其边缘密度为 f (t) ,本文通过 f (t)的核估计构造出θ的函数的经验 Bayes估计 ,并证明在一定的条件下是渐近最优的 (a.0 .)     

一类积分方程组正解的对称性和单调性

本文讨论积分方程组（？）解的性质,其中G_α是α阶贝塞尔位势核,0≤β〈α（n-α＋β）/n,1/（q＋1）＋1/（r＋1）〉（n-α＋β）/n,1/（r＋1）＋1/（p＋1）〉（n-α＋β）/n.我们用积分形式的移动平面法证明上述积分方程组的正解是径向对称且单调的.     

高阶混合中立型微分方程的振动性质（英文）

研究一类高阶混合中立型微分方程：x（t）＋ax（t-γ）-bx（t＋σ）]~（（m））＋δ（q（t）x（t-g）＋p（t）x（t＋h））=0,其中a,b,γ,σ,g,h是正常数,P,q∈C（R~＋,R~＋）,δ=士1,m≥1是整数.得到了方程振动的判据.     

对称熵损失下的一类指数族刻度参数估计

In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L（θ, δ） = v（θ/δ ＋ δ/θ - 2）. An exact form of the minimum risk equivariant estimator under symmetric entropy loss is given, and the minimaxity of the minimum risk equivariant estimator is proved. The results with regard to admissibility and inadmissibility of a class of linear estimators of the form cT（X） ＋ d are given, where T（X） Gamma（v, θ）.     

Estimation of the entropy of a multivariate normal distribution

Motivated by problems in molecular biosciences wherein the evaluation of entropy of a molecular system is important for understanding its thermodynamic properties, we consider the efficient estimation of entropy of a multivariate normal distribution having unknown mean vector and covariance matrix. Based on a random sample, we discuss the problem of estimating the entropy under the quadratic loss function. The best affine equivariant estimator is obtained and, interestingly, it also turns out to be an unbiased estimator and a generalized Bayes estimator. It is established that the best affine equivariant estimator is admissible in the class of estimators that depend on the determinant of the sample covariance matrix alone. The risk improvements of the best affine equivariant estimator over the maximum likelihood estimator (an estimator commonly used in molecular sciences) are obtained numerically and are found to be substantial in higher dimensions, which is commonly the case for atomic coordinates in macromolecules such as proteins. We further establish that even the best affine equivariant estimator is inadmissible and obtain Stein-type and Brewster–Zidek-type estimators dominating it. The Brewster–Zidek-type estimator is shown to be generalized Bayes.     

Hardy-Littlewood极大函数在加权Orlicz—Morrey空间上的有界性

本文得到了极大函数M_ω)(|f|~p)~1/p和M(f)在加权Orlicz-Morrey空间上的有界性,同时也给出了极大函数M_ω(|f|~p)~1/p在其上有界的必要条件.     

多小波采样定理的拓展

采样定理在数字信号通讯中发挥了十分重要的作用,因为信号通常由它的离散采样数据来恢复.Han Bin等人在[J.Comput.Appl.Math.,2009,227:254-270]中构造了广义插值加细函数向量.本文研究与广义插值加细函数向量有关的采样定理的拓展问题.具体而言,对于已知的广义插值d-加细函数向量φ=(φ_1,…,φ_r)~T,即φe(m/r+k)=δ_kδ_(e-1-m),k∈Z,m=0,1,…,r-1,e=1,…,r我们将构造一组函数{φ_(r+1),…,φ_(dr)},使得φ~ロ=(φ~T,φ_(r+1),…,φ_(dr))~T也是d-加细的,而且满足φ_e(m/(dr)+k)=δ_kδ_(θ_(d,r(e)-m))k∈Z,m=0,1,…,dr-1,e=r+1,…,dr,其中θ_(d,r(e))=e-r+R_(e-1-r,d-1),R_(e-1-r,d-1)=「(e-1-r)/(d-1)」.我们建立与φ~■有关的采样定理.显然,φ的多小波子空间采样定理的适用范围得到了拓展.给出φ~■的多小波子空间采样级数的截断误差估计.     

广义超特殊p-群的自同构群(Ⅱ)

重新确定了广义超特殊p-群G的自同构群的结构.设|G|=p~(2n+m),|ζG|=p~m,其中n≥1,m≥2,Aut_cG是AutG中平凡地作用在ζG上的元素形成的正规子群,则(i)若p是奇素数,则AutG=〈θ〉×Aut_cG,其中θ的阶是(p-1)p~(m-1);若p=2,则AutG=〈θ_1,θ_2〉×Aut_cG,其中〈θ_1,θ_2〉=〈θ_1〉×〈θ_2〉≌Z_(2m-2)×Z_2.(ii)如果G的幂指数是p~m,那么Aut_cG/InnG≌Sp(2n,p).(iii)如果G的幂指数是p~(m+1),那么Aut_cG/InnG≌K×Sp(2n-2,p),其中K是p~(2n-1)阶超特殊p-群(若p是奇素数)或者初等Abel 2-群.特别地,当n=1时,Aut_cG/InnG≌Z_p.     

Combining the data from two normal populations to estimate the mean of one when their means difference is bounded

In this paper we address the problem of estimating θ₁ when , are observed and |θ₁−θ₂|?c for a known constant c. Clearly Y₂ contains information about θ₁. We show how the so-called weighted likelihood function may be used to generate a class of estimators that exploit that information. We discuss how the weights in the weighted likelihood may be selected to successfully trade bias for precision and thus use the information effectively. In particular, we consider adaptively weighted likelihood estimators where the weights are selected using the data. One approach selects such weights in accord with Akaike's entropy maximization criterion. We describe several estimators obtained in this way. However, the maximum likelihood estimator is investigated as a competitor to these estimators along with a Bayes estimator, a class of robust Bayes estimators and (when c is sufficiently small), a minimax estimator. Moreover we will assess their properties both numerically and theoretically. Finally, we will see how all of these estimators may be viewed as adaptively weighted likelihood estimators. In fact, an over-riding theme of the paper is that the adaptively weighted likelihood method provides a powerful extension of its classical counterpart.     

Necessary conditions for dominating the James-Stein estimator

This paper develops necessary conditions for an estimator to dominate the James-Stein estimator and hence the James-Stein positive-part estimator. The ultimate goal is to find classes of such dominating estimators which are admissible. While there are a number of results giving classes of estimators dominating the James-Stein estimator, the only admissible estimator known to dominate the James-Stein estimator is the generalized Bayes estimator relative to the fundamental harmonic function in three and higher dimension. The prior was suggested by Stein and the domination result is due to Kubokawa. Shao and Strawderman gave a class of estimators dominating the James-Stein positive-part estimator but were unable to demonstrate admissiblity of any in their class. Maruyama, following a suggestion of Stein, has studied generalized Bayes estimators which are members of a point mass at zero and a prior similar to the harmonic prior. He finds a subclass which is minimax and admissible but is unable to show that any in his class with positive point mass at zero dominate the James-Stein estimator. The results in this paper show that a subclass of Maruyama's procedures including the class that Stein conjectured might contain members dominating the James-Stein estimator cannot dominate the James-Stein estimator. We also show that under reasonable conditions, the “constant” in shrinkage factor must approachp-2 for domination to hold.