k-正态分布及其应用（英文）

近本文研究了截断随机变量和k-正态分布.利用对数凹函数理论,获得了涉及截断随机变量和截断随机变量的函数的方差的不等式链,推广了涉及正态分布和分层教学模型的一些经典结论.同时在附录部分给出了仿真结果.     

行m-NSD随机变量阵列的完全收敛性

本文研究了行m-NSD随机变量阵列的完全收敛性问题.主要利用m-NSD随机变量的Kolmogorov型指数不等式,获得了行m-NSD随机变量阵列的完全收敛性定理,将Hu等（1998）andSung等（2005）的结果从独立情形推广到了m-NSD随机变量阵列.本文的结论同样推广了Chen等（2008）,Hu等（2009）,Qiu等（2011）和Wang等（2014）的结果.     

两两NQD列的Marcinkiewicz-Zygmund不等式及其应用

本文研究了两两NQD随机变量的Marcinkiewicz-Zygmund不等式及其应用的问题.利用截尾的方法,获得了两两NQD随机变量的p阶(1 ≤ p < 2) Marcinkiewicz-Zygmund不等式结果.作为应用,获得了两两NQD随机变量的两个L^r收敛性结果的简单证明,改进了陈平炎^[10]和Sung^[20]的相应工作.     

随机截断模型下的中心极限定理

在流行病学,生物统计学和天文学中常遇到随机截断数据.在随机截断下,人们关心的随机变量X被另一个随机变量y干扰.只有当X≥y时,才能观测到X和Y.在这个模型下,人们需要用截断数据估计X的分布函数F.本文证明,F的非参数最大似然估计Fn在下述意义下服从中心极限定理.对任何可测函数g(x),√n∫f9(x)[dFn(x)-dF(x)]依分布收敛到均值为零方差为σ2的正态分布.从这个结果可以得出F的各种矩,特征函数等估计的渐近正态性.作为推论,还可以得到Fn在整个直线上的依分布收敛.我们的结果不要求X和Y的分布函数连续,得到的方差公式是简明的.     

判定二维随机变量服从正态分布的一个充分条件

探讨了二维随机变量服从正态分布的一个充分条件.在两个不相关的随机变量的任意正整数线性组合都是正态随机变量的条件下,利用矩生成函数证明了它们分别服从正态分布,且联合分布也是二维正态分布.     

D族负象限相依随机变量和的精致大偏差

在负象限相依结构下, 得到了支撑在 (-∞,∞) 上的 D 族随机变量非中心化以及中心化部分和的精致大偏差. 同时, 还在较弱的条件下, 得到了相应的中心化随机和的精致大偏差.     

随机截断模型下的中心极限定理

在流行病学,生物统计学和天文学中常遇到随机截断数据.在随机截断下,人们关心的随机变量X被另一个随机变量Y干扰.只有当X≥Y时,才能观测到X和Y.在这个模型下,人们需要用截断数据估计X的分布函数F.本文证明,F的非参数最大似然估计Fn在下述意义下服从中心极限定理.对任何可测函数g（x）,n~（1/2）∫g（x）[dFn（x）-dF（x）]依分布收敛到均值为零方差为σ2的正态分布.从这个结果可以得出F的各种矩,特征函数等估计的渐近正态性.作为推论,还可以得到Fn在整个直线上的依分布收敛.我们的结果不要求X和Y的分布函数连续,得到的方差公式是简明的.     

费马q-差分微分方程整函数解的增长性研究

本文研究了费马q-差分微分方程的整函数解的相关问题.利用经典和差分的Nevanlinna理论和函数方程理论的研究方法,获得了q-差分微分方程整函数解增长性的几个结果.     

随机误差的分布机理

研究随机变量误差的分布函数,通过数理变形、推导证明此随机变量精确服从正态分布.     

关于Fermat素数与L-函数的加权均值

本文研究了一类以Fermat素数为模的Dirichlet L-函数加权均值的计算问题. 利用初等方法以及Dirichlet 和的性质, 获得了一个有趣的计算公式.     

Consistency of general bootstrap methods for degenerate -type and -type statistics

We provide general results on the consistency of certain bootstrap methods applied to degree-2 degenerate statistics of U-type and V-type. While it follows from well known results that the original statistic converges in distribution to a weighted sum of centred chi-squared random variables, we use a coupling idea of Dehling and Mikosch to show that the bootstrap counterpart converges to the same distribution. The result is applied to a goodness-of-fit test based on the empirical characteristic function.     

On positive definite quadratic forms in correlatedt variables

Summary In this paper we extend Ruben's [4] result for quadratic forms in normal variables. He represented the distribution function of the quadratic form in normal variables as an infinite mixture of chi-square distribution functions. In the central case, we show that the distribution function of a quadratic form int-variables can be represented as a mixture of beta distribution functions. In the noncentral case, the distribution function presented is an infinite series in beta distribution functions. An application to quadratic discrimination is given.     

Convergence of self-normalized generalizedU-statistics

Conditions are given for almost certain and distribution convergence of self-normalized generalizedU-statistics composed of random variables without particular probabilistic structure. The set of almost certain limit points of some classicalU-statistics is obtained. A variant of theU-statistic involving squares of some of the random variables is also treated. Applications include Martingale differences, stationary sequences, and the classical i.i.d. case where a Marcinkiewicz-Zygmund-type strong law is obtained.     

Consistent nonparametric regression from recursive partitioning schemes

We here extend our results on asymptotically Bayes risk efficient classification to the general regression scenario. More precisely, we find L^p consistent estimators for an arbitrary regression function provided only that the dependent variable has a finite absolute pth moment. The estimators are truncated and untruncated local means derived from recursive partitioning schemes.     

Truncated stable random variables: characterization and simulation

Kanter (Ann Probab 3(4):697–707, 1975) and Chambers et al. (J Am Stat Assoc 71(354):340–344, 1976) developed a method for characterizing and simulating stable random variables, X, using nonlinear transformations involving two independent uniform random variables. Their method is scrutinized to provide a characterization and then develop a method for simulating random variables with distribution P(X ≤ x| X  > a), called here truncated stable random variables. Our characterization is rigorous when the characteristic exponent α ≠ 1. We extend our method to the case that α → 1.     

On multiparameter distributions of order k

A multiparameter negative binomial distribution of order k is obtained by compounding the extended (or multiparameter) Poisson distribution of order k by the gamma distribution. A multiparameter logarithmic series distribution of order k is derived next, as the zero truncated limit of the first distribution. Finally a few genesis schemes and interrelationships are established for these three multiparameter distributions of order k. The present work extends several properties of distributions of order k.     

Some General Characterizations of the Bivariate Gumbel Distribution and the Bivariate Lomax Distribution Based on Truncated Expectations

Recently attempts have been made to characterize probability distributions via truncated expectations in both univariate and multivariate cases. In this paper we will use a well known theorem of Lau and Rao (1982) to obtain some characterization results, based on the truncated expectations of a functionh, for the bivariate Gumbel distribution, a bivariate Lomax distribution, and a bivariate power distribution. The results of the paper subsume some earlier results appearing in the literature.     

<Emphasis Type="Italic">m</Emphasis> Components-admissible solutions of systems of higher-order partial differential equations on <Emphasis Type="Italic">C</Emphasis><Superscript><Emphasis Type="Italic">n</Emphasis></Superscript>

Using value distribution theory and techniques in several complex variables,we investigate the problem of existence of m components-admissible solutions of a class of systems of higher-order partial differential equations in several complex variables and estimate the number of admissible components of solutions.Some related results will also be obtained.     

Concavity and efficient points of discrete distributions in probabilistic programming

We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive lower and upper bounds for the optimal value of probabilistically constrained stochastic programming problems with discrete random variables. The results are illustrated with numerical examples. Received: October 1998 / Accepted: June 2000?Published online October 18, 2000     

Bivariate Markov chain embeddable variables of polynomial type

The primary aim of the present article is to provide a general framework for investigating the joint distribution of run length accumulating/enumerating variables by the aid of a Markov chain embedding technique. To achieve that we introduce first a class of bivariate discrete random variables whose joint distribution can be described by the aid of a Markov chain and develop formulae for their joint probability mass function, generating functions and moments. The results are then exploited for the derivation of the distribution of a bivariate run-related statistic. Finally, some interesting uses of our results in reliability theory and educational psychology are highlighted. Research supported by General Secretary of Research and Technology of Greece under grand PENED 2001.