随机利率下的一类特殊年金

研究在随机利率相互独立条件下的某些延付年金的积累值的计算问题,目的在于研究积累值的期望和方差.研究了在随机利率相互独立条件下的期末付虹式年金,期末付平顶虹式年金,期末付倒虹式年金和期末付倒平顶虹式年金的积累值的期望和方差,并且给出了积累值的期望和方差的计算公式.     

随机利率下的年金的计算

我们考虑随机利率下的一类延付年金在n年后的积累值的计算问题,目的在于研究积累值的期望和方差.本文给出两种方法计算在某些年内一类延付年金的积累值的期望和方差,获得了积累值的方差的递推关系,并且给出了计算公式.     

随机权函数半参数回归模型的导方差统计分析

在回归分析中,观测值的方差齐性只是一个基本的假定,在参数、半参数和非参数回归模型中关于异方差检验和估计问题已有很多研究.本文在冉昊和朱忠义(2004)讨论的半参数回归模型的基础上,用随机参数方法,讨论随机权函数半参数回归模型中的异方差检验问题,得到了方差齐性检验Score统计量,同时,当半参数模型存在异方差时,本文还给出了估计方差的方法.     

Meta回归模型的异常值识别及其修正

基于异常值对异质性参数和回归系数估计同时影响的这一新视角下,文章利用方差加权异常值模型(variance-weight outlier model,VWOM)研究了随机效应Meta回归模型的多个异常值识别及其修正问题。首先,推导出Meta回归VWOM分别使用ML和REML估计方法的Score (SC)检验统计量,并考虑Meta回归VWOM的三种扰动方式,包括全局方差扰动,个体方差扰动和随机误差扰动,证明了三种方差扰动的SC检验统计量是等价的。其次,基于异常值对异质性参数和回归系数估计同时影响的考虑,提出了随机效应Meta回归方差加权异常值修正模型(variance-weight outlier modified model,VWOMM),并给出了VWOMM参数的ML和REML估计迭代算法并进行数值求解。此外,通过随机模拟分析验证了SC检验统计量的尺度和功效。最后,利用两个不同类型效应量异常值识别及其处理的实例分析结果,表明了Meta回归VWOM的SC检验统计量识别效果较为显著,VWOMM能有效改善模型拟合程度,为识别和处理复杂数据的异常值提供了一种新的思路和方法。     

应用方差公式 妙求代数最值

<正>方差不但是统计量,它在代数最值问题上也有着比较广泛的应用,我们可以利用方差定义,根据方差的性质来求代数最值.初中数学教材对方差的定义是:设一组数据为■,其平均数为■,则方差■对上式进一步计算可得■我们知道,     

基于Jeffreys验前的Bayes方差估计修正

分析了基于Jeffreys验前的经典Bayes方差估计以及考虑验前信息可信度情况下Bayes方差估计存在的问题,在一般情况下,其方差估计要大于验前子样和验后子样的方差,这显然是不合理的.这是采用Jeffreys验前和正态共轭分布假设时存在的固有问题.为了解决这一问题,提出了方差估计的修正公式,经过计算验证,其值在验前子样和验后子样方差之间,说明修正公式是合理的.     

空间误差模型的多个异常值得分检验

本文研究了空间误差模型(SEM)中多个异常值的检验问题,基于均值漂移模型和方差加权模型这两种异常值模型给出了得分检验统计量的具体形式及其渐近分布。并应用实例分析验证了检验统计量的有效性,最后给出了修正模型的方法。     

基于变量选择和聚类分析的两阶段异方差模型估计

建模经济学领域中的面板数据,异方差性在所难免.两阶段估计方法是一种较好的研究异方差性的手段,在进行样本分组时,如果仅选定一个自变量作为依据,会导致信息量不完整.本文提出了用变量选择的方法筛选出用于分组的几个变量,之后用κ均值方法进行聚类,进而实现对样本的类别划分,从而可以得到异方差估计.实证显示:在异方差估计精度和拟合值方面,本文提出的方法在有效性和可行性方面优势明显.     

广义空间模型的异常值检验

研究了广义空间模型中单个异常值检验问题.分别在均值漂移模型和方差加权模型下导出了检验统计量的具体形式,并给出了在两种异常值模型下检验统计量的近似分布.最后,通过哥伦布市社区犯罪数据证明了方法的有效性.     

基于随机模型的区域产业资本漂移研究

资本是产业系统的"血液",是产业实现可持续发展的重要内因变量.在利益驱动下,产业资本会随机地发生漂移.采用Markov方法研究区域产业资本漂移这一随机过程,利用产业资本的转移概率得到了产业资本周转时间的均值和方差,也给出了资本支持某产业发展时间的均值和方差,进而推导出了产业资本的产出率和产出值的计算方法,并进行了实证分析.     

Mean Convergence Time of Inhomogeneous Genetic Algorithm with Elitism

The theoretical study of a genetic algorithm (GA) has focused mainly on establishing its convergence in probability and almost always to the global optimum. In this article, we establishsufficient conditions for the finiteness of convergence mean time of the genetic algorithm with elitism. We obtain bounds for the probability of convergence to the global optimum in the first n iterations as a by-product.     

New Reflection Generator for Simulated Annealing in Mixed-Integer/Continuous Global Optimization

To reduce the well-known jamming problem in global optimization algorithms, we propose a new generator for the simulated annealing algorithm based on the idea of reflection. Furthermore, we give conditions under which the sequence of points generated by this simulated annealing algorithm converges in probability to the global optimum for mixed-integer/continuous global optimization problems. Finally, we present numerical results on some artificial test problems as well as on a composite structural design problem.     

Finding maximum likelihood estimators for the three-parameter Weibull distribution

Much work has been devoted to the problem of finding maximum likelihood estimators for the three-parameter Weibull distribution. This problem has not been clearly recognized as a global optimization one and most methods from the literature occasionally fail to find a global optimum. We develop a global optimization algorithm which uses first order conditions and projection to reduce the problem to a univariate optimization one. Bounds on the resulting function and its first order derivative are obtained and used in a branch-and-bound scheme. Computational experience is reported. It is also shown that the solution method we propose can be extended to the case of right censored samples.     

Minimization of stress levels by distribution of elastic parameters in elastic and thermoelastic bodies

We study the problem of minimizing stress levels in an isotropic elastic body by distributing elasticity parameters. We introduce a large class of differentiable functionals that we use to estimate stress levels. We obtain the necessary and sufficient conditions for global optimum and prove the uniqueness of the optimal stress field. We also study the problem of optimization of a thermoelastic body and derive a relation between the optimization results for elastic and thermoelastic bodies.Translated from Matematicheskie Metody i Fiziko-mekhanicheskie Polya, No. 26, pp. 43–50, 1987.     

Convergence of the Simulated Annealing Algorithm for Continuous Global Optimization

A class of simulated annealing algorithms for continuous global optimization is considered in this paper. The global convergence property is analyzed with respect to the objective value sequence and the minimum objective value sequence induced by simulated annealing algorithms. The convergence analysis provides the appropriate conditions on both the generation probability density function and the temperature updating function. Different forms of temperature updating functions are obtained with respect to different kinds of generation probability density functions, leading to different types of simulated annealing algorithms which all guarantee the convergence to the global optimum.     

Local and integral properties of a search algorithm of the stochastic approximation type

An optimum random-search algorithm is considered. The convergence conditions to the greatest increase (local properties) and convergence to the point of extremum (integral properties) of a function by optimizing in the presence of noise, are found. The results are used for finding a global extremum of a multiextremal function.     

A global optimization method for nonconvex separable programming problems

Conventional methods of solving nonconvex separable programming (NSP) problems by mixed integer programming methods requires adding numerous 0–1 variables. In this work, we present a new method of deriving the global optimum of a NSP program using less number of 0–1 variables. A separable function is initially expressed by a piecewise linear function with summation of absolute terms. Linearizing these absolute terms allows us to convert a NSP problem into a linearly mixed 0–1 program solvable for reaching a solution which is extremely close to the global optimum.     

Convergence guarantees for generalized adaptive stochastic search methods for continuous global optimization

This paper presents some simple technical conditions that guarantee the convergence of a general class of adaptive stochastic global optimization algorithms. By imposing some conditions on the probability distributions that generate the iterates, these stochastic algorithms can be shown to converge to the global optimum in a probabilistic sense. These results also apply to global optimization algorithms that combine local and global stochastic search strategies and also those algorithms that combine deterministic and stochastic search strategies. This makes the results applicable to a wide range of global optimization algorithms that are useful in practice. Moreover, this paper provides convergence conditions involving the conditional densities of the random vector iterates that are easy to verify in practice. It also provides some convergence conditions in the special case when the iterates are generated by elliptical distributions such as the multivariate Normal and Cauchy distributions. These results are then used to prove the convergence of some practical stochastic global optimization algorithms, including an evolutionary programming algorithm. In addition, this paper introduces the notion of a stochastic algorithm being probabilistically dense in the domain of the function and shows that, under simple assumptions, this is equivalent to seeing any point in the domain with probability 1. This, in turn, is equivalent to almost sure convergence to the global minimum. Finally, some simple results on convergence rates are also proved.     

Global optimization of nonconvex factorable programming problems

In this paper, we consider a special class of nonconvex programming problems for which the objective function and constraints are defined in terms of general nonconvex factorable functions. We propose a branch-and-bound approach based on linear programming relaxations generated through various approximation schemes that utilize, for example, the Mean-Value Theorem and Chebyshev interpolation polynomials coordinated with a Reformulation-Linearization Technique (RLT). A suitable partitioning process is proposed that induces convergence to a global optimum. The algorithm has been implemented in C++ and some preliminary computational results are reported on a set of fifteen engineering process control and design test problems from various sources in the literature. The results indicate that the proposed procedure generates tight relaxations, even via the initial node linear program itself. Furthermore, for nine of these fifteen problems, the application of a local search method that is initialized at the LP relaxation solution produced the actual global optimum at the initial node of the enumeration tree. Moreover, for two test cases, the global optimum found improves upon the solutions previously reported in the source literature. Received: January 14, 1998 / Accepted: June 7, 1999?Published online December 15, 2000     

Revisiting the big valley search space structure in the TSP

The solution space of the travelling salesman problem under 2-opt moves has been characterized as having a big-valley structure, in which the evaluation of a tour is positively correlated to the distance of the tour from the global optimum. We examine the big-valley hypothesis more closely and show that while the big-valley structure does appear in much of the solution space, it breaks down around local optima that have solutions whose evaluation is very close to that of the global optimum; multiple funnels appear around local optima with evaluations close to the global optimum. The appearance of multiple funnels explains why certain iterated local search heuristics can quickly find high-quality solutions, but fail to consistently find the global optimum. We then investigate a novel search operator, which is demonstrated to have the ability to escape funnels at evaluations close to the global optimum.