一类离散分布参数的经验Bayes估计的收敛速度

In this paper we consider a family of discrete distributions f_θ(x)dμ(x), and suppose that the Bayes estimate of φ(θ) with respect to the priori distribution H∈H has a form dH(x) =(?)a_k(x)f(x+k)/f(x). where f(x)=∫f_θdH(θ) . we construct asequence of empirical Bayes estimates and establish its rate of convergence, and prove that under suitable conditions this rate of convergence can arbitrarily close to 1. we also give a counter-example to the main Theorem 2.1 of [5], and then declare that the "Theorem" does not hold.     

至多一个变点的Γ分布的统计推断及在金融中的应用

对至多一个变点的Γ分布,即X1,X2…,Xn为一列相互独立的随机变量序列,且X1,X2,…,X[n(τ)0]i.i.d～Γ(x;ν1,λ1),X[n(τ)τ0]+1,X[n(τ)0]+2,…,Xn i.i.d～Γ(x;ν2,λ2),其中(τ)0未知,称(τ)0为该序列的变点.在利用第一型极值分布逼近文中提出统计量的分布的基础上,给出了变点(τ)0估计(τ)的相合性及强弱收敛速度.最后给出了在金融序列上的应用.     

线性模型中误差方差估计的强相合性的充要条件

本文在只假定误差独立而不必同分布的条件下,得出了基于残差平方和的误差方差估计的强相合性的充要条件。这个条件与试验点列完全无关。这样,就解决了文献[1]中提出的问题。     

双边截断参数的最小方差无偏估计

设g(θ_1,θ_2)为定义在△上的已知函数,x_1,…,x_n为取自(1)的iid.样本。本文的目的是寻求g的UMVUE估计,并研究其方差的数量级,附带地对比较简单的单边情况,我们将指出文献中有关结果的不足之处,并给出严格的条件。     

球面密度估计的积分平方误差的中心极限定理

设X ₁ , X₂,…, X_n是在R^Q+1的q-维单位球面Ω_Q取值的随机变量X的iid观测值(q≥1), 其球面概率密度函数为f(X).设f_n ( X)=n^-1(h)K((1- X^ＴXⁱ)／h²)是f(X)的核估计. 在较弱的条件下建立了f_n的积分平方误差的中心极限定理.     

线性模型中误差方差估计的收敛速度

在通常的线性模型y_i=x′_iβ+e_i(i=1,…,n,…)中,设σ~2=Var(e_i),由前n次观测值y_1,…,y_n,可得基于残差平方和的σ~2的估计(?)_n~2,本文证明了:若随机误差序列独立同分布,则对某个t≥1,E|e_1~2|~t<∞的充要条件为,对任给的ε>0,这样,对于(?)_n~2-σ~2的收敛速度,得到与同分布独立和情形同样优良的结果。     

NORMAL APPROXIMATION FOR FINITEPOPULATION U-STATISTICS

In this article, the general central limit theorem and Berry-Esseen bounds for finite-populationU-statistics with degree m are established under the very weak conditions, These resultssubstantially improve those of Zhao and Chen's (1987).     

Strong consistency of M-estimates in linear models

The strong consistency of M-estimates of the regression coefficients in a linear model under some mild conditions is established, which is an essential improvement over the relevant results in the literature on the moment condition. Especially, in some important circumstances, onlyE|ψ(e_k)|^q for some q > 1 is needed, where ψ{e_k} is some score function of random error.     

线性模型中误差方差估计的分布的渐近展开

<正> §1.引言许宝騄教授在他的著名的工作[1]中,得到了样本方差1/(n-1)sum from i=1 to n (X_i-(?))~2(经过规则化)的分布的渐近展开.本文的目的是把许教授的结果推广到线性模型中误差方差的基于残差平方和的估计.考虑线性模型Y_i=x′_iβ+e_i,i=1,2,…,n,…. (1)此处,{x_i}为一串已知的 p 维向量(试验点列),β=(β_1,…,β_p)′为未知的回归系数向     

Convergence Rates of the Distributions of Error Variance Estimates in Linear Models

The best possible rate of convergence of the distributions of error variance estimates in linear models, based on the residual sum of squares, is obtained under weakest possible conditions.