序限制下的统计预测

In this paper,a statistical prediction problem under ordered location and scale parameters are considered.Double-shrinkage predictors are given which use all the available data and improve on single-shrinkage predictors,and hence the best equivariant predictors.     

Linex Unbiasedness in a Prediction Problem

A statistical prediction problem under LINEX loss function is considered. Some results about LINEX-unbiased predictor are derived and the best LINEX-unbiased predictor is given. We also show that the best risk-unbiased predictor is equal to the best equivariant predictor in the location family.     

多维正态分布均值在序约束下的假设检验

在序限制下的统计推断是统计分析中的一个重要领域，保序回归理论在这个领域中起着关键性的作用。多维保序回归是一维保序回归的推广，本文给出了k=2,p=2时多维保序回归的求解方法。令Xij,j=1,2…，n是来自总体为二维正态分布N（μi，Λ)的样本，这是μi是未知的，Λ是已知的，i=1,2。令μ=（μ1,μ2)，-={（μ1,μ2)|μ1,μ∈R^2,}-0={(μ1,μ2)|μ1≤μ2,μ1，μ2∈R^2}。μ1≤μ2表示μ2-μ1的每一个分量为非负。本文也讨论了假设检验问题H0:μ∈-0,H1:μ∈-0=---0(H0是零假设）。     

伞形序约束下参数的一个广义信息准则

The detection of the configuration of parameters is one of the most important problems im statistical studies. It is well known that the Akaike‘s information criterion (AIC) is a key tool for this problem (see [1]). Usually, the AIC is defined as: AIC(μ^) := l(μ^) -p, where l(μ^) is the log-likelihood with the maximum likelihood estimator (MLE) μ for μ,     

Estimation of an Exponential Quantile under a General Loss and an Alternative Estimator under Quadratic Loss

Estimation of the quantile + of an exponential distribution with parameters (, ) is considered under an arbitrary strictly convex loss function. For obeying a certain condition, the inadmissibility of the best affine equivariant procedure is established by exhibiting a better estimator. The LINEX loss is studied in detail. For quadratic loss, sufficient conditions are given for a scale equivariant estimator to dominate the best affine equivariant one and, when exceeds a lower bound specified below, a new minimax estimator is identified.     

Componentwise estimation of ordered parameters ofk (≥2) exponential populations

We consider the estimation of ordered parameters ofk ( 2) exponential distributions by improving upon the usual estimators. TheBrewsterzidek technique is used to find sufficient conditions for an estimator of _i and/or _i (i=1,...,k), to be inadmissible with respect to the MSE criterion where _i and _i are the location and scale parameters respectively of thei-th exponential population. Using these sufficient conditions improved estimators of _i and/or _i (i=1,...,k) are obtained.     

带有非中心不完全椭球约束的线性模型中线性估计的可容许性

本文研究了线性模型(Y,Xβ,σ2V V≥0)在非中心不完全椭球约束:(β-β0)’N(β-β0)≤σ2,N≥0下椭球中心β0对线性估计的可容许性的影响,证明了对于具有某种结构的β1和β2,线性模型(Y,Xβ,σ2V,V≥0)在非中心不完全椭球约束:(β-β1)’N(β-β1)≤σ2,N≥0与非中心不完全椭球约束:(β-β2)’N(β-β2)≤σ2,N≥0下的可容许线性估计类是相同的．     

一个n有序集组的计数问题

In this paper, we discuss the counting problem of an order n-group of set (A1 ,A2 ,… ,An) which satisfies ∪^ni=1Ai={a,a2,…,am} and one of the following:(1)∩^ni=1Ai=Φ;(2)∩^ni=1Ai={b1,b2,…,bk};(3)∩^ni=1A1包含{b1,b2,…,bk};(4)Ai≠Φ(i=1,2,…,k），We solve these problems by element analytical method.     

Note on an Open Problem of Higher Order Nonlinear Evolution Equations

In view of a new idea on initial conditions, an open problem of nonlinear evolution equations with higher order, which was given by J. L. Lions, is solved. Effect of our results is shown on an example.     

不完全椭球约束下多元线性模型线性估计的可容许性

本文研究了多元线性模型当未知参数受不完全椭球约束$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$时线性估计的可容许性问题.具体而言,我们研究了约束$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$中$N$和非中心点$\Theta_1$对线性估计的可容许性的影响.主要结果表明在两个不同的不完全椭球约束条件$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$与$\mbox{tr}(\Theta-\Theta_2)'N(\Theta-\Theta_2)\leq\sigma^2$ 下,当$\Theta_1$和$\Theta_2$满足一定的关系时,可容许的齐次线性估计类是相同的.     

Admissibility of prediction intervals

Admissibility of prediction intervals is considered in a specified family. It is shown that the best invariant prediction interval is strongly admissible in a location family and in a scale family. Though the similar result has not been obtained for a location and scale family, the best invariant prediction interval for a normal distribution is shown to be weakly admissible.     

Sufficient conditions under which SSD- and MR-efficient sets are identical

Three approaches are commonly used for analyzing decisions under uncertainty: expected utility (EU), second-degree stochastic dominance (SSD), and mean-risk (MR) models, with the mean–standard deviation (MS) being the best-known MR model. Because MR models generally lead to different efficient sets and thus are a continuing source of controversy, the specific concern of this article is not to suggest another MR model. Instead, we show that the SSD- and MR-efficient sets are identical, as long as (a) the risk measure satisfies both positive homogeneity and consistency with respect to the Rothschild and Stiglitz (1970) definition(s) of increasing risk and (b) the choice set includes the riskless asset and satisfies a generalized location and scale property, which can be interpreted as a market model. Under these conditions, there is no controversy among MR models and they all have a decision-theoretic foundation. They also offer a convenient way to compare the estimation error related to the empirical implementation of different MR models.     

无限级整函数在角域内的取值和增长性

借助熊庆来的无限级,将Nevanlinna建立的有限级整函数在角域内的取值和增长性的结果推广到无限级.作为应用,研究了高阶超越整函数系数微分方程f~((k))+A_k-2(z)f~((k-2))+…+A_1(x)f'+A_0(z)f=0解的径向振荡.     

正态总体均值与标准差比在简单半序约束下的最大似然估计

本文考虑两个正态总体均值和方差是未知参数,均值与标准差的比在简单半序约束下,且样本个数不同时,计算均值和标准差的最大似然估计的问题.给出了计算均值和标准差的最大似然估计的方法.同时还给出了三个总体均值与标准差的比在简单半序约束下求均值和标准差的最大似然估计的计算方法,并给出了迭代算法的模拟结果.     

保持序和等价关系的自然偏序变换半群

设X为一个集合,■_X为X上的全变换半群.设E是X上的一个等价关系,定义T_E(X)={f∈■_X:■(x,y)∈E,(f(x),f(y))∈E},则T_E(X)是由等价关系E所确定的■_X的子半群.本文中,所考虑的集合X是一个有限全序集,同时E是非平凡的且所有的E-类都是凸集.显然■_E(X)={f∈T_E(X):■_x,y∈X,x≤y蕴涵f(x)≤f(y)}是T_E(X)的一个子半群.我们赋予■_E(X)自然偏序并讨论何时■_E(X)中的两个元素是关于这个偏序是相关的,然后确定■_E(X)中那些关于≤是相容的元素.此外,还描述了极大(极小)元和覆盖元.     

无穷积分余项与其被积函数比值的收敛阶

对一类函数的无穷积分余项与该函数的比值得到当x趋于无穷大时的收敛阶,这类函数是幂函数与指数函数的乘积函数,并将其应用到Mittag-Leffler函数.同时考虑了对应的级数情形.     

Improving on the minimum risk equivariant estimator of a location parameter which is constrained to an interval or a half-interval

For location families with densitiesf ₀(x−θ), we study the problem of estimating θ for location invariant lossL(θ,d)=ρ(d−θ), and under a lower-bound constraint of the form θ≥a. We show, that for quite general (f ₀, ρ), the Bayes estimator δ _U with respect to a uniform prior on (a, ∞) is a minimax estimator which dominates the benchmark minimum risk equivariant (MRE) estimator. In extending some previous dominance results due to Katz and Farrell, we make use of Kubokawa'sIERD (Integral Expression of Risk Difference) method, and actually obtain classes of dominating estimators which include, and are characterized in terms of δ _U . Implications are also given and, finally, the above dominance phenomenon is studied and extended to an interval constraint of the form θ∈[a, b]. Research supported by NSERC of Canada.     

Connections of Higher Order and Product Preserving Functors

In this paper we consider a product preserving functor F of order r and a connection of order r on a manifold M. We introduce horizontal lifts of tensor fields and linear connections from M to F(M) with respect to . Our definitions and results generalize the particular cases of the tangent bundle and the tangent bundle of higher order.     

三阶奇摄动非线性边值问题

利用微分不等式理论,研究了某一类三阶奇摄动非线性边值问题。以二阶边值问题的已知结果为基础,引入Volterra型积分算子,建立了三阶非线性边值问题的上下解方法。在适当条件下,构造出具体的上下解,得出解的存在性和渐进估计。结果表明这种技巧也为三阶奇摄动边值问题的研究提出了崭新的思路。最后举例验证文中定理的正确性。