Cluster Algorithms for Spatial Point Processes with Symmetric and Shift Invariant Interactions

Abstract

In this article, Swendsen–Wang–Wolff algorithms are extended to simulate spatial point processes with symmetric and stationary interactions. Convergence of these algorithms is considered. Some further generalizations of the algorithms are discussed. The ideas presented in this article can also be useful in handling some large and complicated systems.     

A Note on Mixing Properties of Certain Associated Processes

We prove that under appropriate conditions a sequence of associated, integer-valued random variables is also strongly mixing. The almost sure invariance principle and the central limit theorem for integer-valued associated processes are also discussed.     

具有突变率的广义生-灭过程——随机单调性、Feller性及可配称性

本文给出了具有突变率的广义生－灭过程的随机单调性、Feller性及可配称性的充要条件。     

随机环境中受控两性分枝过程的概率性质

本文介绍了一类配对依赖人口数的随机环境中受控两性分枝过程,研究了它的马氏性,对偶律及其概率母函数的性质.     

点过程模型中向量参数极大似然估计的渐近性质

点过程是一个应用广泛的统计模型，在医学，社会学，经济学，电子与通信科学以及软件与硬件可靠性等许多科学领域都能找到应用点过程的例子，在这些实际应用中，一般是根据问题的实际背景假定模型具有一定的参数形式，然后根据观测数据给出未知参数的极大似然估计值以推断事物发展的客观规律，我们知道，一种估计量是否收敛以及收敛速度的快慢，是决定这种估计量好坏的最为重要的标准，本文对于一般的点过程模型中向量参数极大似然估计（MLE）首先给出了一个保证其强相合的较为广泛的充分性条件，然后在进一步的条件下得到了重对数型的收敛速度。     

On a Functional Operation Generating Convex Functions,Part 2: Algebraic Properties

Algebraic properties of the functional operation introduced in Part 1 of this paper (Ref. 1) are considered. In essence, the functional operation is shown to be associative, right distributive with respect to addition, and left distributive with respect to epigraphic sum.     

The Geometric Markov Renewal Processes with Application to Finance

We introduce the geometric Markov renewal processes as a model for a security market and study this processes in a series scheme. We consider its approximations in the form of averaged, merged and double averaged geometric Markov renewal processes. Weak convergence analysis and rates of convergence of ergodic geometric Markov renewal processes are presented. Martingale properties, infinitesimal operators of geometric Markov renewal processes are presented and a Markov renewal equation for expectation is derived. As an application, we consider the case of two ergodic classes. Moreover, we consider a generalized binomial model for a security market induced by a position dependent random map as a special case of a geometric Markov renewal process.     

Algorithmic Approach to the Extinction Probability of Branching Processes

The extinction probability of a branching process is characterized as the solution of a fixed-point equation which, for a fairly general class of Markovian branching processes, is vector quadratic. We address the question of solving that equation, using a mixture of algorithmic and probabilistic arguments. We compare the relative efficiency of three iterative methods based on functional iteration, on the basis of the probabilistic interpretation of the successive iterations as well as on the basis of traditional rate of convergence analysis. We illustrate our findings through a few numerical examples and conclude by showing how they extend to more complex systems.     

扩散过程的生存时间及其对条件扩散的应用

设τＤ是扩散过程在Ｒｄ中区域Ｄ上的生存时间．本文给出τＤ指数阶矩有限的一个充分条件．这里的区域与扩散算子都是一般的．作为应用，改进了Ｇａｏ（１９９５）关于条件扩散的主要结果．     

两指标点过程的停止及σ-域

本文给出两指标点过程的跳线刻划、停止方法,论述了跳线的可测性及点过程有关的σ-域的结构和特征。     

Convergence of Point Processes with Weakly Dependent Points

For each n≥1, let {X _j,n }_1≤j≤n be a sequence of strictly stationary random variables. In this article, we give some asymptotic weak dependence conditions for the convergence in distribution of the point process $N_{n}=\sum_{j=1}^{n}\delta_{X_{j,n}}For each n≥1, let {X _j,n }_1≤j≤n be a sequence of strictly stationary random variables. In this article, we give some asymptotic weak dependence conditions for the convergence in distribution of the point process N_n=?_j=1ⁿd_Xj,nN_{n}=\sum_{j=1}^{n}\delta_{X_{j,n}} to an infinitely divisible point process. From the point process convergence we obtain the convergence in distribution of the partial sum sequence S _n =∑ _j=1 ⁿ X _j,n to an infinitely divisible random variable whose Lévy measure is related to the canonical measure of the limiting point process. As examples, we discuss the case of triangular arrays which possess known (row-wise) dependence structures, like the strong mixing property, the association, or the dependence structure of a stochastic volatility model.     

Optimal Control of Point Processes with Noisy Observations: The Maximum Principle

This paper studies the optimal control problem for point processes with Gaussian white-noised observations. A general maximum principle is proved for the partially observed optimal control of point processes, without using the associated filtering equation . Adjoint flows—the adjoint processes of the stochastic flows of the optimal system—are introduced, and their relations are established. Adjoint vector fields , which are observation-predictable, are introduced as the solutions of associated backward stochastic integral-partial differential equtions driven by the observation process. In a heuristic way, their relations are explained, and the adjoint processes are expressed in terms of the adjoint vector fields, their gradients and Hessians, along the optimal state process. In this way the adjoint processes are naturally connected to the adjoint equation of the associated filtering equation . This shows that the conditional expectation in the maximum condition is computable through filtering the optimal state, as usually expected. Some variants of the partially observed stochastic maximum principle are derived, and the corresponding maximum conditions are quite different from the counterpart for the diffusion case. Finally, as an example, a quadratic optimal control problem with a free Poisson process and a Gaussian white-noised observation is explicitly solved using the partially observed maximum principle. Accepted 8 August 2001. Online publication 17 December, 2001.     

拟单位根过程的渐近性质

West[4],Phillips[3]的关于AR（1）过程的一些有关渐近正态性的结果推广到了拟整过程，所得的结果更具一般性，从而可用来进行单位根据检验及协整检验的功效分析。     

A Functional Interpretation of Cycloid Processes: Application to the Study of Asymptotic Behaviour

We show that the classic Chapman–Kolmogorov equations of certain Markovian transition semigroups on finite state spaces have a formal analogy, of a homologic nature, in terms of cycloids ₁, ..., _B, and positive numbers w₁, ..., w_B. The collection _k ,w _k completely determines a Markov process {_n}, called a cycloid process, admitting an invariant probability distribution, and decomposes its distribution Prob(_n = , _{n + 1} = ) into a linear expression. The latter is further used in the study of the asymptotic behaviour of the cycloid process.     

The Equivalence of Ergodicity and Weak Mixing for Infinitely Divisible Processes

The equivalence of ergodicity and weak mixing for general infinitely divisible processes is proven. Using this result and [9], simple conditions for ergodicity of infinitely divisible processes are derived. The notion of codifference for infinitely divisible processes is investigated, it plays the crucial role in the proofs but it may be also of independent interest.     

Functional Convergence of Linear Processes with Heavy-Tailed Innovations

We study convergence in law of partial sums of linear processes with heavy-tailed innovations. In the case of summable coefficients, necessary and sufficient conditions for the finite dimensional convergence to an \(\alpha \)-stable Lévy Motion are given. The conditions lead to new, tractable sufficient conditions in the case \(\alpha \le 1\). In the functional setting, we complement the existing results on \(M_1\)-convergence, obtained for linear processes with nonnegative coefficients by Avram and Taqqu (Ann Probab 20:483–503, 1992) and improved by Louhichi and Rio (Electr J Probab 16(89), 2011), by proving that in the general setting partial sums of linear processes are convergent on the Skorokhod space equipped with the \(S\) topology, introduced by Jakubowski (Electr J Probab 2(4), 1997).     

Generating a Random Collection of Discrete Joint Probability Distributions Subject to Partial Information

In this paper, we develop a practical and flexible methodology for generating a random collection of discrete joint probability distributions, subject to a specified information set, which can be expressed as a set of linear constraints (e.g., marginal assessments, moments, or pairwise correlations). Our approach begins with the construction of a polytope using this set of linear constraints. This polytope defines the set of all joint distributions that match the given information; we refer to this set as the “truth set.” We then implement a Monte Carlo procedure, the Hit-and-Run algorithm, to sample points uniformly from the truth set. Each sampled point is a joint distribution that matches the specified information. We provide guidelines to determine the quality of this sampled collection. The sampled points can be used to solve optimization models and to simulate systems under different uncertainty scenarios.     

一类推广后的双Poisson风险模型破产概率的鞅分析

本文将双复合Poisson风险模型推广到资金利率和通货膨胀率下带干扰的新模型,运用鞅分析方法获得了其破产概率所满足的Lundberg不等式及其一般表达式。     

The Local Spectral Properties of Extended Hamilton Operators

In this paper, we introduce the class of extended Hamilton operators and study various properties of this class. We examine the decomposability of extended Hamilton operators. In addition,we prove that an extended Hamilton operator with property(δ) is subscalar. Finally, we consider Weyl type theorems of this class.