一种对称损失函数下刻度参数的最优同变估计

本文研究刻度参数分布族(1/σ)f(x/σ)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其最小最大性。     

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．     

一类嵌套多元指数分布相关参数的矩型估计

本文研究一类具有三个相关参数的嵌套多元指数分布,其生存函数为其中,0相似文献   

在一类特殊的随机截断下分布函数的估计

本文讨论了在一类特殊的随机截断下分布函数的估计及其渐近正态性.     

一类二元相关威布尔分布及其参数估计

考虑生存函数为的二元威布尔分布,提出θ1,θ2,α,δ的估计并讨论了它们的渐近性,最后作模拟计算,得出了参数估计的渐近效．     

二元相依Weibu11分布的参数估计(英文)

考虑生存函数为F(x_1,x_2)=exp{-[(x_1~(1/α)/θ_1)~(1/δ) ((x_2~(1/α)/θ_2)~(1/δ)]~δ},x_i>0,θ_i>0,i=1,2,α>0,0<δ≤1的二无相依Weibull分布。基于在Ⅰ型截尾情形下两个元件与串联系统的寿命试验数据,本文给出了未知参数θ_1,θ_2,α和δ的估计,并讨论了这些估计的渐近性质。本文还给出了随机模拟的结果。     

一类多维指数分布的参数估计

考虑生存函数为(F)(x1,x2,…,xn)=P{X1＞x1,…,Xn＞xn}=exp{-[n∑i=1(xi/θi)1/δ]δ}(0＜xi＜∞,0＜δ≤ 1,0＜θi＜∞,i=(1,n))的一类多维指数分布,给出了它的密度函数的表示式,并讨论了它的性质.提出了相关参数δ的估计(^δ),证明了(^δ)有相合性和渐近正态性,得到了(^δ)的渐近方差σ2δ.最后还给出了若干随机模拟的结果.     

右删失左截断情形下分布函数的分位数估计

文中考虑了右删失左截断数据情形下分布函数的分位数估计，讨论了该估计的渐近性质并获得了它的强弱Ｂａｈａｄｕｒ类型的表示定理。利用此Ｂａｈａｄｕｒ表示定理很容易获得该分位数估计的渐近正态性及置信区间等结果。     

多参数离散指数族参数渐近最优的经验Bayes估计的收敛速度

本文构造了多参数离散指数族参数的渐近最优的经验Bayes（EB）估计,若记B_n（δ_n,G）为δn的全面Bayes风险,R_G最小Bayes风险,则在某些条件下c_1n~(-1）2成立,其中c_1,c_2为正的常数,     

相协样本分布函数光滑估计的正态逼近速度

在平稳相协样本下,讨论分布函数光滑估计的一致渐近正态性.在较合理的条件下给出了分布函数光滑估计的一致渐近正态性的收敛速度,这个速度几乎达到n~(-1/4).     

非正则双边截断分布族点估计的中偏差

本文研究了某一类非正则双边截断分布族的参数估计,利用( X(1),X(n))的联合分布函数及应用Taylor渐近展开的方法,得到了它的未知参数(θ1,θ2)满足中偏差原理,且求出了其精确的速率函数表达式,它的表达式不同于一般的速率函数.     

方差未知时两样本正态混合模型齐-性的修正似然比检验

本文讨论了方差未知时检验两样本正态混合模型齐一性的修正似然比统计量的极限性质,证明了原假设下修正似然比统计量的渐近分布为自由度为1的卡方分布.     

双边截断情形下QH 统计量的渐近分布

ＣｈｅｎＬａｎｄＳｈａｐｉｒｏＳＳ［１］提出如下检验正态性的统计量其中,Ｘ１ｎ,Ｘ２ｎ,…,Ｘｎｎ为容量为n的样本的次序统计量,Ｈ为标准正态分布函数的逆函数．本文在一定的条件下得到了双边截断情形下ＱＨ统计量的渐近分布．     

Asymptotic Behavior of Solutions of Some Linear Difference Equations with Oscillatory Decreasing Coefficients

We investigate the asymptotic behavior of solutions of the system x ( n +1)=[ A + B ( n ) V ( n )+ R ( n )] x ( n ), n S n 0 , where A is an invertible m 2 m matrix with real eigenvalues, B ( n )= ~ j =1 r B j e i u j n , u j are real and u j p ~ (1+2 M ) for any M ] Z , B j are constant m 2 m matrices, the matrix V ( n ) satisfies V ( n ) M 0 as n M X , ~ n =0 X Á V ( n +1) m V ( n ) Á < X , ~ n =0 X Á V ( n ) Á 2 < X , and ~ n =0 X Á R ( n ) Á < X . If AV ( n )= V ( n ) A , then we show that the original system is asymptotically equivalent to a system x ( n +1)=[ A + B 0 V ( n )+ R 1 ( n )] x ( n ), where B 0 is a constant matrix and ~ n =0 X Á R 1 ( n ) Á < X . From this, it is possible to deduce the asymptotic behavior of solutions as n M X . We illustrate our method by investigating the asymptotic behavior of solutions of x 1 ( n +2) m 2(cos f 1 ) x 1 ( n +1)+ x 1 ( n )+ a sin n f n g x 2 ( n )=0 x 2 ( n +2) m 2(cos f 2 ) x 2 ( n +1)+ x 2 ( n )+ b sin n f n g x 1 ( n )=0 , where 0< f 1 , f 2 < ~ , 1/2< g h 1, f 1 p f 2 , and 0< f <2 ~ .     

Exploring functional CLT confidence intervals for a population mean in the domain of attraction of the normal law

We take a Student process that is based on independent copies of a random variable \({X}\) and has trajectories in the function space \({D}\)[0, 1]. As a consequence of a functional central limit theorem (FCLT) for this process, with \({X}\) in the domain of attraction of the normal law, we consider convergence in distribution of five functionals of this process and derive respective asymptotic confidence intervals for the mean of \({X}\). We conclude that the obtained intervals have higher finite-sample coverage probabilities, or shorter expected lengths, than those of a classical asymptotic confidence interval, \({I_0}\), that follows simply from the asymptotic normality of the Student \({t}\)-statistic. Thus, the five FCLT based intervals may present reasonable alternatives to \({I_0}\).     

On location estimation for LARCH processes

We consider location estimation when the error process is a stationary LARCH process with long memory in the second moments. The asymptotic distribution of the sample mean and nonlinear M-estimators of the location parameter are derived. Essential assumptions for obtaining asymptotic normality with -rate of convergence are symmetry of the innovation distribution and skew-symmetry of the ψ-function.     

On the asymptotics of supremum distribution for some iterated processes

In this paper, we study the asymptotic behavior of supremum distribution of some classes of iterated stochastic processes \(\{X(Y(t)) : t \in [0, \infty )\}\), where \(\{X(t) : t \in \mathbb {R} \}\) is a centered Gaussian process and \(\{Y(t): t \in [0, \infty )\}\) is an independent of {X(t)} stochastic process with a.s. continuous sample paths. In particular, the asymptotic behavior of \(\mathbb {P}(\sup _{s\in [0,T]} X(Y (s)) > u)\) as \(u \to \infty \), where T>0, as well as \(\lim _{u\to \infty } \mathbb {P}(\sup _{s\in [0,h(u)]} X(Y (s)) > u)\), for some suitably chosen function h(u) are analyzed. As an illustration, we study the asymptotic behavior of the supremum distribution of iterated fractional Brownian motion process.     

双边截断分布族中的卷积定理与渐近有效性

在具有共同支撑的分布族与单边截断分布族中,Ｂｉｃｋｌｅ Ｐ．Ｊ, Ｉｂｒａｇｉｍｏｖ　Ｉ． Ａ． 与 Ｈａｓｍｉｎｓｋｉｉ Ｒ． Ｚ．证明了两个重要的卷积定理．在本文中,我们考虑如下的双边截断分布族：ｄＰθ（ｘ）＝ ｆ（ｘ; θ１, θ２）Ｉ（θ１≤ｘ≤ θ２）ｄｘ,其中θ＝（θ１, θ２）, θ１＜ θ２为未知 待估参数．在较弱的条件下,我们得到了关于此分布族的卷积定理．并且,基于此卷 积定理的结论,提出了一个参数函数渐近有效性的定义．在本文结束之时,对于一个 双边截断分布族,给出了具有此渐近有效性的参数函数的估计．     

回归函数改良核估计的渐近分布

设(X1,Y1),…,(Xn,Yn)是来自二元总体(X,Y)的样本,若EY＜∞,则回归函数m(x)＝E(Y｜X＝x)存在。在本文中,考虑m(x)的改良核估计     

条件经验随机过程的渐近性质

设(Xi,Yi)(i=1,2,…,n)是来自总体(X,Y)的样本(独立同分布),其中X∈R1,Y∈Rq.M(x y)是Y=y时X的条件分布,Mnkn(x y)为M(x y)的第kn个最近邻域的经验分布估计量,讨论条件经验过程Sn(t,x,y)=kn12(Mnkn(x y)-M(x y))的渐近性质,得出在适当条件下,对固定的y,Sn(t,x,y)(x,t为参数)弱收敛于某一G aussian过程S(.).