两个和K个分布函数相符的自助法检验

两个和Ｋ个分布函数相等的检验在统计领域中,长期以来一直受到关注,为了克服高维空产中数据点的稀疏性,并且对一般情况进行处理,我们提出了几个投影追踪类型的统计量,并得到了这些统计量的极限分布的一些结果,对一些自助法逼近的性质进行了研究,更进一步地,由于计算的原因,对统计量的逼近,我们采用了数论方法,并进行了一些模拟试验。     

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．     

基于投影绝对中位离差的分布相等性检验

为检验两个分布的相等，我们给出基于投影寻踪、Bootstrap方法和数论方法的统计量，并讨论它的极限分布和Bootstrap逼近。最后我们给出一些模拟。     

投影寻踪型Cramér-von Mises-Smirrnov统计量(英文)

为了检验一个总体分布是否服从所给定的分布Ｆ（ｘ），Ｃｒａｍéｒ－ｖｏｎＭｉｓｅｓ－Ｓｍｉｒｎｏｖ统计量是一种常用的重要工具．对于一维分布，计算表明确切分布很快趋于极限分布．当样本量大于３时，确切分布与极限分布之差就很小，当总体分布是连续分布时该统计量的极限分布与总体分布无关．本文讨论总体分布为高维分布时用投影寻踪的方法建立Ｃｒａｍéｒ－ｖｏｎＭｉｓｅｓ－Ｓｍｉｒｎｏｖ统计量，对此统计量尾部概率上界及极限分布，包括当样本很大，维数很高时的极限性质，自助法是否能逼近极限分布，用Γ分布或者Γ分布混合逼近确切分布是否能行等问题作了探讨，并提出了一些未解决的问题．     

AR(1)序列的样本均值分布的随机加权逼近

随机加权方法是郑忠国近期提出的逼近统计量分布的新方法.目前,已有许多文章讨论了此方法对一些统计量分布的逼近问题.但这些结果仅限于对独立随机样本的研究.本文将此方法应用于相依随机序列——AR(1)序列的样本均值,并得到了和独立情形同样好的结果.     

L─统计量的Bootstrap逼近

Ｌ─统计量的Ｂｏｏｔｓｔｒａｐ逼近任哲，陈明华（六安师范专科学校，六安２３７０１２）本文提出了Ｌ──统计量的一种Ｂｏｏｔｓｔｒａｐ逼近，并讨论了这种逼近的相合性及其逼近的精确性。一、引言及主要定理设兄，ｉ＞１为来自分布为Ｆ的ｉ．ｉ．ｄ．样本，以Ｘ．；...     

随机PP Kolmogorov多维拟合优度检验乃其Bootstrap逼近

本文提出了一种基于随机选择投影方向的PP型棉球等高分布族的拟合优度检验，其特点是计算上较通常的PP检验统计量简单．得到了其检验统计量在零假设下的极限分布，讨论了其Bootstrap逼近及逼近的相容性．     

L—统计量的随机加权逼近

本文用随机加权法构造了L—统计量的未知分布的逼近,我们讨论了这种随机加权逼近的相合性及逼近精确度。     

线性模型中M检验原假设分布的随机加权逼近

在线性模型中M-方法可以用于线性假设检验, 其中M检验、Wald检验和Rao的计分型检验是最常用的检验准则. 但是在计算这些检验的临界值时都涉及到未知参数的估计. 在本文中我们利用随机加权的方法来逼近这些检验的原假设分布. 结果表明在原假设和局部对立假设之下随机加权统计量的渐近分布与原检验统计量在原假设之下的渐近分布相同. 因此我们不需要对冗余参数进行估计,利用随机加权的方法就可以得到这些检验的临界值. 而且在局部对立假设之下可以实现对功效的计算. 当取不同的误差分布和不同的随机权时, 我们对本文的方法进行了蒙特卡洛模拟. 结果表明用随机加权方法来逼近原假设分布是非常精确的.     

来自正态总体的常相关样本几个统计量的分布及其应用

本文指出了从一些生物群体中进行抽样时,由于个体间亲缘关系的存在,常常得不到一个简单子样。因此进行统计推断时不能直接应用由简单样本而得到的一些统计结论,并根据一些生物群体的实际情况,给出了来自正态总体的常相关样本几个统计量的分布,可应用于生物统计的某些方面,并用实例对这种应用予以说明。     

TESTS OF COVARIANCE MATRIX BY USING PROJECTION PURSUIT AND BOOTSTRAP METHOD

Testing equality of covariance matrix has long been an interesting issue in statistics inference, To overcome the sparseness of data points in high-dimensional space and deal with the general cases, the author suggests several projection pursuit type statistics. Some results on the limiting distidbutions of the statistics are obtained. Some properties of bootstrap approximation are investigated. Furthermore, for computational reasons an approximation for the statistics based on number-theoretic roethod is applied. Several simulation experiments are performed.     

Bootstrap tests for the equality of distributions

Testing equality of two and k distributions has long been an interesting issue in statistical inference. To overcome the sparseness of data points in high-dimensional space and deal with the general cases, we suggest several projection pursuit type statistics. Some results on the limiting distributions of the statistics are obtained. Some properties of Bootstrap approximation are investigated. Furthermore, for computational reasons an approximation for the statistics the based on Number theoretic method is applied. Several simulation experiments are performed.     

On testing for homogeneity of the covariance matrices

Testing equality of covariance matrix ofk populations has long been an interesting issue in statistical inference. To overcome the sparseness of data points in a high-dimensional space and deal with the general cases, we suggest several projection pursuit type statistics. Some results on the limiting distributions of the statistics are obtained. Some properties of Bootstrap approximation are investigated. Furthermore, for computational reasons an approximation which is based on Number theoretic method for the statistics is adopted. Several simulation experiments are performed.     

ON SOME TESTS FOR ELLIPTICAL SYMMETRY

In this paper, some test statistics of Kolmogorov type and Cramer-von Mises type based on projection pursuit technique are proposed for testing the sphericity problem of a high-dimensional distribution. The limiting distributions of the test statistics are derived under the null hypothesis and any fixed alternative. The asymptotic properties of Bootstrap approximation are investigated. Furthermore, for computational reasons, an approximation for the statistics, based on number theoretic method, is suggested.     

基于随机加权的空间聚集性检验及其在基础教育师资均衡性评价中的应用

使用空间统计检验方法研究北京基础教育资源分配的均衡性问题.对于空间分布均匀性的检验,常用的统计量是Moran's I统计量.但基于Moran's I统计量做推断的时候,人们往往用渐进正态分布或者用Bootstrap反复抽样得到经验分布来进行.提出使用随机加权法进行统计量的经验检验.Jin和Lee(2014)文中得出基于Bootstrap的Moran's I统计量满足一致逼近和渐进正态等性质.采用类似的统计工具证明了基于随机加权得到的统计量的渐进分布也满足这些良好性质.填补了用随机加权法在空间统计量的推断中理论保证的空白.通过模拟研究,证明了所提算法的有效性.方法应用于北京基础教育的师资-适龄儿童数比例,师资-在校生数比例的空间聚集性检验中得到了良好的应用,并与其它检验方法所得结论进行比较.结论显示在不同相邻概念(地理相邻、政策空间相邻)下,方法得到的结论符合常理.     

Simple linear approximations to the likelihood equation for combining evidence in multiple 2×2 tables: A critique of conventional procedures

Summary The conventional procedures for a common odds ratio in multiple 2×2 tables are explored and critiqued. Three types of linear approximation to the likelihood equations under some models of common measures of association are used to derive the popular conventional estimators and test statistics. Some of them are derived using the model of the common standardized difference which is an unacceptable measure. The derivation provides us with some characteristics of the procedures. The advantages of procedures based on the conditional and unconditional likelihoods are discussed. The Institute of Statistical Mathematics     

椭球检验与统计量的渐近尾部性

In this paper,some test statistics of Kolmogorov type and Cramervon Mises type based on projection pursuit technique are proposed for testing the sphericity problem of a high-dimensional distribution. The limiting distributions of the test statistics are derived under the null hypothesis. The asymptotic properties of Bootstrap approximation are investigated and the tail behaviors of the statistics are studied.     

Approximation algorithms for nonnegative polynomial optimization problems over unit spheres

We consider approximation algorithms for nonnegative polynomial optimization problems over unit spheres. These optimization problems have wide applications e.g., in signal and image processing, high order statistics, and computer vision. Since these problems are NP-hard, we are interested in studying on approximation algorithms. In particular, we propose some polynomial-time approximation algorithms with new approximation bounds. In addition, based on these approximation algorithms, some efficient algorithms are presented and numerical results are reported to show the efficiency of our proposed algorithms.     

Tests of elliptical symmetry and the asymptotic tail behavior of the statistics

In this paper, some test statistics Of Kolmogorov type and Cramervon Mises type based on projection pursuit technique are proposed for testing the sphericity problem of a high-dimensional distribution. The limiting distributions of the test statistics are derived under the null hypothesis. The asymptotic properties of Bootstrap approximation are investigated and the tail behaviors of the statistics are studied.     

Quantitative Approximation to the Ordered Dirichlet Distribution under Varying Basic Probability Spaces

An approximate expansion of a sequence of ordered Dirichlet densities is given under the set-up with varying dimensions of the relating basic probability spaces. The problem is handled as the approximation to the joint distribution of an increasing number of selected order statistics based on the random sample drawn from the uniform distribution U(0, 1). Some inverse factorial series to the expansion of logarithmic function enable us to give quantitative error evaluations to our problem. With the help of them the relating modified K-L information number, which is defined on an approximate main domain and different from the usual ones, is accurately evaluated. Further, the proof of the approximate joint normality of the selected order statistics is more systematically presented than those given in existing works. Concerning the approximate normality the modified affinity and the half variation distance are also evaluated.