重复测量试验模型参数似然比检验及其功效分析

本文给出了在重复测量试验模型下, 当受试对象观测向量的协方差矩阵$\Sigma$为复合对称阵时,参数的似然比检验统计量; 给出该检验在原假设下的渐近零分布和在备择假设下的渐近非零分布;并就其功效进行了分析.     

基于随机矩阵理论的高维数据球形检验

本文基于随机矩阵理论,研究了一般总体的高维协方差矩阵的球形检验.当样本量小于数据维数时,经典的似然比检验方法在球形检验中已无法使用.通过引入样本协方差矩阵谱分布的高阶矩,构造出一个新的检验统计量,并给出其在零假设下的渐近分布.模拟实验表明所提出的统计量在控制第一类错误概率的基础上能有效提高检验功效,对于Spiked模型效果尤为显著.     

有缺失数据的正态母体参数的后验分布及其抽样算法

在缺失数据机制是可忽略的、先验分布是逆矩阵Γ分布的假设下,利用矩阵的cholesky分解和变量替换方法,本文导出了有单调缺失数据结构的正态分布参数的后验分布形式.进-步用后验分布的组成特点,构造了单调缺失数据结构的正态分布的协方差矩阵和均值后验分布的抽样算法.     

关于能量距离的两点注记（英文）

本文讨论了能量距离的两个问题.类似Brownian协方差的讨论提出了Brownian距离的定义,并证明了Brownian距离与能量距离的一致性.给出了配对变量的能量距离的表示,并探讨了将能量距离用于配对样本同分布的检验问题时原假设下的渐近分布理论.最后通过一个简单的数值模拟说明基于能量距离的配对样本的分布差异的检验方法比传统的t检验及Wilcoxon符号秩检验更有效.     

线性模型中F-检验的稳健性

本文讨论了一般线性模型中关于均值参数β的线性假设基于广义最小二乘估计的F-检验统计量的稳健性问题.主要研究了当误差的协方差矩阵含有参数时,设计阵可以列降秩情况下的F-检验统计量的稳健性,得到了F(V(θ))为该假设下F-检验统计量的误差协方差矩阵的最大类.并讨论了分块线性模型中,关于分块参数的线性假设的F-检验统计量的稳健性.     

面板数据交互固定效应模型的协方差矩阵检验

本文研究了面板数据交互固定效应模型中协方差矩阵的检验问题.首先依据模型协方差矩阵迹的估计构造检验统计量,检验协方差矩阵是否为单位矩阵,或是单位矩阵的常数倍.然后在一定正则条件下,证明了检验统计量的渐近性质,并说明所提出的检验方法不依赖于误差分布.最后,通过模拟研究对本文的检验方法进行评价,说明所提检验方法在高维面板数据下仍然有效.     

增长曲线模型（RRS）的Bayes影响分析

本文讨论具有Rao简单协方差结构(RSS)的增长曲线模型中未知参数矩阵的Bayes影响分析问题,在无信息先验分布假设下,K-L距离被用来评估指定响应阵对参数矩阵的后验分布的影响程度。     

增长曲线模型（一般协方差结构）的Bayes影响分析

本文讨论具有一般协方差结构的增长曲线模型中未知参数矩阵的Ｂａｙｅｓ影响分析问题．在无信息先验分布假设下,Ｋ－Ｌ距离被用来评估指定响应阵对参数矩阵的后验分布的影响程度．     

高维数据空间的协方差矩阵迹的比率相合性

在处理高维数据的检验和分类等问题时,涉及到协方差矩阵的估计.而在高维数据领域,协方差矩阵估计的精度将对诸如检验和分类等问题起到非常重要的影响.主要考虑多样本条件下协方差矩阵的比率相合性问题,证明了两样本和三样本情况下的高维数据协方差矩阵比率相合性.     

多元线性模型中回归系数矩阵非齐次线性估计的可容许性

该文研究了协方差矩阵未知的多元线性模型中,二次矩阵损失函数下回归系数矩阵可估线性函数的非齐次线性估计的可容许性.不需正态分布的假设,作者给出矩阵非齐次线性估计在线性估计类中可容许的充要条件;在正态分布的假设下,作者给出矩阵非齐次线性估计在一切估计组成的估计类中可容许的充分条件.     

位置参数分布族中Fiducial分布与后验分布的关系

本文考虑本质位置参数分布族中,参数的Fiducial分布与后验分布的等同问题．首先讨论了如何给出Fiducial分布,分析结果表明以分布函数形式给出Fiducial分布要比密度函数形式合理,同时,证明了所给的Fiducial分布具有频率性质．然后,研究在参数受到单侧限制时,Fiducial分布与后验分布等同的问题,给出的充要条件是分布族为指数分布族,此时,先验分布是一个广义先验分布,它不能被Lebesgue测度控制．最后,证明了在参数限制在一个有限区间内时,Fiducial分布与任何先验(包括广义先验分布)下的后验分布不等同．     

On generalized binomial and multinomial distributions and their relation to generalized Poisson distributions

Summary The binomial and multinomial distributions are, probably, the best known distributions because of their vast number of applications. The present paper examines some generalizations of these distributions with many practical applications. Properties of these generalizations are studied and models giving rise to them are developed. Finally, their relationship to generalized Poisson distributions is examined and limiting cases are given.     

统计学上三大分布推导方法

讨论了如何求随机变量函数分布的方法,然后用两种方法推出统计学上三个重要分布的概率分布密度函数.方法独特新颖.     

On Moments of Folded and Truncated Multivariate Normal Distributions

Recurrence relations for integrals that involve the density of multivariate normal distributions are developed. These recursions allow fast computation of the moments of folded and truncated multivariate normal distributions. Besides being numerically efficient, the proposed recursions also allow us to obtain explicit expressions of low-order moments of folded and truncated multivariate normal distributions. Supplementary material for this article is available online.     

The Möbius distribution on the disc

A simple new family of distributions is proposed which has support the unit disc in two dimensions. The density functions of the family are unimodal, monotonic or uniantimodal. The bivariate symmetric beta distributions, which include the uniform distribution, are special cases, but many members of the family are skew. The distributions have three parameters, one controlling orientation, one controlling degree of concentration and the third controlling skewness, or more precisely off-centredness. Importantly, these parameters are globally orthogonal. An illustrative example of fitting the model to data is given. Conditional and marginal distributions are considered. The new distributions are compared favourably with an earlier suggestion of the same author.     

New examples of heavy-tailed O-subexponential distributions and related closure properties

Let L and S denote the classes of distributions with long tails and subexponential tails respectively. Let OS denote the class of distributions with O-subexponential tails, which means the distributions with the tails having the same order as the tails of their 2-fold convolutions. In this paper, we first construct a family of distributions without finite means in L∩OS?S. Next some distributions in L∩OS?S, which possess finite means or even finite higher moments, are also constructed. In connection with this, we prove that the class OS is closed under minimization of random variables. However, it is not closed under maximization of random variables.     

Can Data Recognize Its Parent Distribution?

This study is concerned with model selection of lifetime and survival distributions arising in engineering reliability or in the medical sciences. We compare various distributions—including the gamma, Weibull, and lognormal—with a new distribution called geometric extreme exponential. Except for the lognormal distribution, the other three distributions all have the exponential distribution as special cases. A Monte Carlo simulation was performed to determine sample sizes for which survival distributions can distinguish data generated by their own families. Two methods for decision are by maximum likelihood and by Kolmogorov distance. Neither method is uniformly best. The probability of correct selection with more than one alternative shows some surprising results when the choices are close to the exponential distribution.     

常见连续型统计分布的一点注记

正态分布是概率论与数理统计中最重要的一个分布,本文讨论了常见的连续型统计分布与标准正态分布间的关系,结果表明:几乎所有的常见连续型统计分布都是标准正态分布的函数.     

On some compound distributions with Borel summands

The generalized Poisson distribution is well known to be a compound Poisson distribution with Borel summands. As a generalization we present closed formulas for compound Bartlett and Delaporte distributions with Borel summands and a recursive structure for certain compound shifted Delaporte mixtures with Borel summands. Our models are introduced in an actuarial context as claim number distributions and are derived only with probabilistic arguments and elementary combinatorial identities. In the actuarial context related compound distributions are of importance as models for the total size of insurance claims for which we present simple recursion formulas of Panjer type.     

Simple and efficient algorithms for computing exact cumulative discrete probabilities and its inverses

Summary  This paper deals with the computation of exact cumulative probabilities of discrete distributions and its inverses. For the computation of cumulative probabilities an efficient and universal algorithm of 15 lines is presented, which can be applied to the most important discrete distributions (e.g. the binomial, the poisson and the hypergeometric distribution). With a slight modification an algorithm of 20 lines is obtained for the calculation of the respective inverse distributions. The accuracy of both algorithms can be specified. Both algorithms are simple, very fast and numerically stable even if the sample size is one billion.