两个总体相等的广义似然比检验

利用广义似然比检验的原理,首先求出广义似然比统计量的极限分布,然后给出了两个总体相等的广义似然比检验方法,并且给出了随机模拟结果.     

两个回归函数相等的Bootstrap检验

两个回归参数相等性检验一直是统计界感兴趣的问题之一．在这篇文章中，四个检验统计量被用于度量两曲线的差异，在原假设下统计量的分布采用向量数据的重复抽样来逼近，并给出了—些模拟结果．     

多个正态总体均值相等的VDR检验

本文将随机估计由一维参数扩展至多维参数,基于随机估计的密度函数提出VDR检验.在总体方差已知和未知的两种情形下,本文讨论多个正态总体均值是否相同的VDR检验过程,而且得到精确的检验.单因素方差分析是VDR检验的特例.模拟研究表明,VDR检验是一个普遍适用的方法.     

两个伽玛分布的同质性检验

对于两个伽玛分布,Γ(α_1,β_1)和Γ(α_2,β_2),讨论了统计假设:H_0:α_1=α_2,β_1=β_2H_1:α_1≠α_2或β_1≠β_2,基于Hellinger距离与参数的最大似然估计,建立了一个检验统计量.在一定的条件下证明了统计量渐近服从自由度为2的卡方分布.最后用随机模拟的方法研究了所建立的统计量的稳健性,并且与似然比检验统计量进行了比较.     

两个正态总体的似然比检验

对于两个正态总体N（μ1,σ1^2)和N(μ2，σ2^2)，本文讨论了统计假设H0：μ1=μ，σ1^2=σ2^2←→H1：μ1≠μ，或σ1^2≠σ2^2。给出了似然比检验统计量分布及相应的临界值表，并对其临界值表的计算给出了详细的讨论。     

复高阶差分方程解

利用亚纯函数的Nevanlinna值分布理论,讨论了两类复高阶差分方程亚纯解的存在性和增长级问题,一类复差分方程的Malmquist型定理被建立,另一类复差分方程解的增长级得到估计.例子表明了我们的结果是精确的.     

相依假设下样本相关矩阵最大元的渐近分布

设{Xk,i;k≥1,i≥1)是一随机变量组列,令{pn;n≥1)是一正整数序列,满足c1≤n/pn≤c2,其中c1,c2是正实数.假设{Xk,i;k≥1,i≥1}满足一些相依条件,得到了Ln的渐近分布,这里Ln= ,以及表示(X1,i,…,Xn,i)'和(X1,j,…,Xn,j)'间的Pearson相关系数.     

复振荡理论中关于超级的角域分布

设f_1和f_2是微分方程f″+A(z)f=0的两个线性无关的解,其中A(z)是无穷级整函数且超级σ_2(A)=0．令E=f_1f_2．本文研究了微分方程f″+A(z)f=0的解在角域中的零点分布,得出E的超级为+∞的Borel方向与零点聚值线的关系．     

复合Poisson过程参数的检验

当风险为非同质时，索赔次数的分布可用复合Poisson过程来描述，Hofmann分布族可用于复合Poisson过程中索赔次数的研究，本文讨论了Hofmann分布的相参数的检验问题，得到了它的检验统计量及其渐近分布。     

关于一般复微分方程的代数体函数解

利用亚纯函数的Nevanlinna值分布理论,我们研究了一般复微分方程式代数体允许解的存在性问题,得到了一些结要。     

多项分布总体的相等对增凸序问题的多样本检验

概率分布间随机序在实践中已经得到了广泛的应用,而且似然比检验是用以检验涉及随机序问题的最普遍的检验方法．但是,关于多个多项式总体间的增凸序约束的统计推断问题并没有得到充分发展．多样本的增凸序对无约束的检验问题已被研究．然而,多总体的相等性对增凸序的假设检验问题似乎更有研究意义．并且分布的相等对随机序的假设检验问题往往是统计学家最为普遍地考虑．对多样本的情况,本文考虑了分布的相等对增凸序的假设检验问题,并且获得似然比检验统计量的零渐近分布,它是一组x~2分布随机变量的加权和,即■~2分布．     

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）。     

检验两个部分线性模型中非参函数相等

We propose the test statistic to check whether the nonparametric func-tions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.     

A Method for Testing Nested Point Null Hypotheses Using Multiple Bayes Factor

As a flexible Bayesian test criterion for nested point null hypotheses, asymmetric and multiple Bayes factors are introduced in the form of a modified Savage-Dickey density ratio. This leads to a simple method for obtaining pairwise comparisons of hypotheses in a statistical experiment with a partition on the parameter space. The method is derived from the fact that in general, the asymmetric Bayes factor can be written as the product of the Savage-Dickey ratio and a correction factor where both terms are easily estimated by means of posterior simulation. Analyses of a censored data problem and a serial correlation problem are illustrated for the method. For these cases, the method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required tests.     

Testing for increasing convex order in several populations

Increasing convex order is one of important stochastic orderings. It is very often used in queueing theory, reliability, operations research and economics. This paper is devoted to studying the likelihood ratio test for increasing convex order in several populations against an unrestricted alternative. We derive the null asympotic distribution of the likelihood ratio test statistic, which is precisely the chi-bar-squared distribution. The methodology for computing critical values for the test is also discussed. The test is applied to an example involving data for survival time for carcinoma of the oropharynx.     

Test homogeneity of order-restricted means

Suppose that an order restriction is imposed among several p-variate normal mean vectors. We are interested in testing the homogeneity of these mean vectors under this restriction. This problem is an extension of Sasabuchi, Tanaka and Tsukamoto＇s problem.     

关于既约非负矩阵优势比的界

本文改进了Ａ．Ｍ．Ｏｓｔｒｏｗｓｋｉ和佟文廷的结果,得到了关于既约非负矩阵优势比的新界．     

正态分布的小样本数据的相容性检验理论和方法

对现有的任意分布的相容性检验理论和方法进行了详细分析和探讨 .指出了现存的一些问题 .并研究了正态分布的小样本容量的相容性检验问题的理论 ,提出了新的检验方法 .检验效果较好 .     

On Testing for the Number of Components in a Mixed Poisson Model

Poisson mixtures are usually used to describe overdispersed data. Finite Poisson mixtures are used in many practical situations where often it is of interest to determine the number of components in the mixture. Identifying how many components comprise a mixture remains a difficult problem. The likelihood ratio test (LRT) is a general statistical procedure to use. Unfortunately, a number of specific problems arise and the classical theory fails to hold. In this paper a new procedure is proposed that is based on testing whether a new component can be added to a finite Poisson mixture which eventually leads to the number of components in the mixture. It is a sequential testing procedure based on the well known LRT that utilises a resampling technique to construct the distribution of the test statistic. The application of the procedure to real data reveals some interesting features of the distribution of the test statistic.