分布值函数和分布的乘法（Ⅱ）

在超分布的代数运算的基础上定义了分布的U-乘积,具体计算了(δ~((m))(x)·δ~((n))(x))U、δ~((x))_U~n和(x~m·(δ(x))~(m+p))_U。利用U-乘积定义了分布的广义乘积,还引进了δ(x)在点a的值的概念。最后,研究了广义乘积在近代物理学中的两个应用:(1)给出了非线性波动方程的因果解;(2)给出了跃迁几率计算过程中使用的公式(δ(x))~2=δ(0)δ(x)的确切含义及证明。     

分布乘法问题评述

论述了研讨分布乘法的两种基本途径，还分析了八十年代关于分布乘积研究的两个特点．文章结尾指出了九十年代分布乘法理论可能的发展趋势．     

独立随机变量乘积的分布

得到了两个独立的任意分布随机变量乘积的分布函数的计算公式,该公式推广了参考文献[1]中定理1的结果.     

左截断右删失数据下光滑分布函数估计效率

研究了左截断右删失数据下光滑分布函数估计,并获得了其渐近性质.在MSE意义下,给出了光滑分布函数估计与经验估计(即乘积限估计)的相对亏量,证明了在一定的条件下,光滑分布估计要优于经验分布估计,并通过模拟说明了光滑分布函数估计比乘积限估计更加有效.     

一种判定两个随机变量的乘积(和)服从何种分布的方法

本文介绍求两个独立Beta分布随机变量的乘积服从何种分布的方法.先随机产生大量贝塔分布随机变量的乘积,用数值拟舍来分析并验证拟合结果.借助SAS软件系统,用回归分析找出分布参数间的相关关系,并作图验证结论的正确性.     

离散型与连续型分布互为对偶的根源

互为对偶的离散型分布与连续型分布,可以看作是由同一个函数——源函数产生的。源函数的正线性组合、乘积和负导数,仍然是源函数。源函数揭示了互为对偶的分布的分布函数之间的相互关系,并能用来求随机变量的数字特征、特征函数、概率母函数、分布的最大值和参数的极大似然估计.     

截断和随机乘积的渐近性质

对一列独立同分布平方可积的随机变量序列{Xn,n≥1},当随机变量的分布具有中尾分布时,讨论了其截断和Tn(a)的随机乘积的渐近正态性质,其中Tn(a)=Sn-Sn(a),n=1,2,…,Sn(a)=n∑ j=1 XjI{Mn-a＜Xj≤Mn},a为某一大于零的常数'Mn=max 1≤k≤n{Xk}.     

随机截断下PL估计的强表示式

本文在随机左截断情形下,研究了分布函数不连续时,分布函数的乘积限估计（PL估计）Fn(x）的一致强表示式,得到与分布函数连续情形下相同的结果。     

非自治离散系统的分布混沌性

该文在非自治离散系统中定义了分布混沌,研究了映射序列f_n,∞=(f_n,f_n+t,…),_n∈N（N为自然数集）的混沌行为,讨论了f_n,∞的分布混沌性是否意味着乘积系统f_n,∞^[m]（m为正整数）的分布混沌性,或者后者的分布混沌性是否意味着前者的分布混沌性.     

相互独立的m值随机变量和的极限分布定理

利用ｍ值随机变量的特征函数,在一定条件下,得到了相互独立的ｍ值随机变量和的极限分布均匀的充要条件;再结合无穷乘积的有关性质,给出了相互独立的ｍ值随机变量和极限分布均匀分布的充分条件,特别当ｍ为素数Ｐ时,所得的充分条件易于验证,且不难满足。     

A note on estimating parameters in two-parameter Pareto distributions

Pareto distributions are used extensively in modelling income distributions. Estimation of parameters is revisited in two-parameter Pareto distributions. The method of quantile estimates using the elemental estimates and the method of product spacings are applied to the two-parameter Pareto distributions. A comparative study between the maximum likelihood method, the unbiased estimates which are functions of the maximum likelihood method, the minimum mean squared error method, the method of moments, the method of quantile estimation, the method of quantile estimation using the elemental estimates and the method of product spacings is presented.     

On the Distributions of the State Sizes of Closed Continuous Time Homogeneous Markov Systems

The evolution of the state sizes of a closed continuous-time homogeneous Markov system is determined by the convolution of multinomial distributions expressing the number of transitions between the states of the system. In order to investigate the distributions of the state sizes, we provide the computation of their moments, at any time point, via a recursive formula concerning the derivative of the moments. The basic result is given by means of a new vector product which is similar to the Kronecker product. Finally, a formula for the computation of the state sizes distributions is given.     

DISTRIBUTIONS OF LIMIT CYCLES FOR POLYNOMIAL VECTOR FIELDS (P_2, Q_3)

The distributions of limit cycles of cubic vector fields (P2, Q3) are considered in this paper, where P2 and Q3 are polynomials of x and y of order two and three, respectively. It is possibly seven different distributions of limit cycles given in [1]. We now prove that in which three kinds of distributions are impossible and other four kinds all can be realized by concrete vector fields of (P2,Q3). Some related results are also given.     

On fundamental skew distributions

A new class of multivariate skew-normal distributions, fundamental skew-normal distributions and their canonical version, is developed. It contains the product of independent univariate skew-normal distributions as a special case. Stochastic representations and other main properties of the associated distribution theory of linear and quadratic forms are considered. A unified procedure for extending this class to other families of skew distributions such as the fundamental skew-symmetric, fundamental skew-elliptical, and fundamental skew-spherical class of distributions is also discussed.     

The tail probability of the product of dependent random variables from max-domains of attraction

In this article, we investigate the tail probability of the product of finitely many non-negative dependent random variables. They follow distributions from max-domains of attraction of extreme value distributions and their dependence is modeled via a multivariate Farlie–Gumbel–Morgenstern distribution. For each of the Fréchet, Gumbel and Weibull cases, we obtain an explicit asymptotic formula for the tail probability of the product. Our study extends a few known results in the literature.     

Optimal Scaling of Random Walk Metropolis Algorithms with Non-Gaussian Proposals

The asymptotic optimal scaling of random walk Metropolis (RWM) algorithms with Gaussian proposal distributions is well understood for certain specific classes of target distributions. These asymptotic results easily extend to any light tailed proposal distribution with finite fourth moment. However, heavy tailed proposal distributions such as the Cauchy distribution are known to have a number of desirable properties, and in many situations are preferable to light tailed proposal distributions. Therefore we consider the question of scaling for Cauchy distributed proposals for a wide range of independent and identically distributed (iid) product densities. The results are somewhat surprising as to when and when not Cauchy distributed proposals are preferable to Gaussian proposal distributions. This provides motivation for finding proposal distributions which improve on both Gaussian and Cauchy proposals, both for finite dimensional target distributions and asymptotically as the dimension of the target density, d → ∞. Throughout we seek the scaling of the proposal distribution which maximizes the expected squared jumping distance (ESJD).     

ON THE INVARIANT SUBMANIFOLDS OF RIEMANNIAN PRODUCT MANIFOLD

In this paper, the vertical and horizontal distributions of an invariant submanifold of a Riemannian product manifold are discussed. An invariant real space form in a Riemannian product manifold is researched. Finally, necessary and sufficient conditions are given on an invariant submanifold of a Riemannian product manifold to be a locally symmetric and real space form.     

The hyper-Dirichlet process and its discrete approximations: The butterfly model

The aim of this paper is the study of some random probability distributions, called hyper-Dirichlet processes. In the simplest situation considered in the paper these distributions charge the product of three sample spaces, with the property that the first and the last component are independent conditional to the middle one. The law of the marginals on the first two and on the last two components are specified to be Dirichlet processes with the same marginal parameter measure on the common second component. The joint law is then obtained as the hyper-Markov combination, introduced in [A.P. Dawid, S.L. Lauritzen, Hyper-Markov laws in the statistical analysis of decomposable graphical models, Ann. Statist. 21 (3) (1993) 1272-1317], of these two Dirichlet processes. The processes constructed in this way in fact are in fact generalizations of the hyper-Dirichlet laws on contingency tables considered in the above paper. Our main result is the convergence to the hyper-Dirichlet process of the sequence of hyper-Dirichlet laws associated to finer and finer “discretizations” of the two parameter measures, which is proved by means of a suitable coupling construction.