关于参数型copula函数的拟合检验

在金融和保险中,copula函数是一种构造多元相关分布函数的有力工具．然而,怎样选择一个适当的copula函数用于拟合数据,并没有找到统一的方法．因此,基于copula函数的经验分布,我们提出了一种用于检验具有某种特定参数结构的copula函数拟合数据优良性的方法,并得到了此检验的渐近性质．由于该检验统计量的极限分布依赖未知参数,我们采用非参数蒙特卡罗方法确定临界值．我们做了一个简单的模拟来验证本文提出的检验方法的功效．     

居民收入分布函数的实证研究

收入分布函数的估计方法主要有参数估计法与非参数估计的方法.利用参数估计方法,依据黑龙江省及国家城镇居民人均可支配收入数据,分别采用极大似然法与分段计算分布总体中的参数,确定收入分布函数,然后根据分布函数与实际数据的拟合状况,验证黑龙江省及国家人均可支配收入服从对数正态分布,但是参数的确定方式决定了拟合的有效性.     

关于梯度法的一些性质

若干年来,函数的无约束极值问题的研究,由于实际的需要又引起人们极大的兴趣。利用函数的梯度来构造各种求极值的方法也进行了广泛的研究。在只利用函数的梯度的迭代法中,最简单的就是最速下降法即梯度法,其计算程序如下:若欲求极小的目标函数     

混合模型中失效率函数的性质与混合分布族

在失效数据的Ｃｏｘ混合模型中混合后的失效率函数的性质会与基准失效率函数的性质有很大的不同。一个非常重要的现象是：在一些常用的混合分布下,当基准失效率为上升函数时,混合后的失效率却可能为下降函数。本文在Ｃｏｘ混合模型下讨论混合后的失效率函数的增减性质与基准失效率及混合分布的关系。在此基础上推荐一个参数族－平移共轭稳定分布族,作为混合分布族。此分析族包含一个熟知的分布,用它作混合分布族可以拟合具有不同     

上证指数收益率分布的拟合

本文旨在讨论上证指数收益率序列的分布特征 ,通过对上证指数 1 997年 5月 2 3日至 2 0 0 1年 7月1 3日 ,总计 1 0 0 0多个交易日的统计分析 ,发现上证指数收益率的分布具有“尖峰、厚尾”的特性 .我们试图用文献 [1 ]给出 Lévy flight来拟合其分布 ,但结果并不理想 .为此我们提出了一种类似 Weibull分布的函数来拟合上证指数收益率分布 ,模拟结果较好 .同时 ,我们按通常的方法对尾部作了截尾处理 ,以更接近实际数据在尾部表现出来的“厚尾”现象     

Marshall-Olkin扩展重尾分布的刻画

利用Marshall-Olkin提出构造分布的方法,以重尾分布F作为基础,提出了Marshall-Olkin扩展重尾分布G,根据常见重尾分布子族的定义及其等价关系,分析了F与G的相关性质,对于重尾分布族,G具有封闭性,尾等价性,同时在连续型分布情形下,讨论了F与G的密度函数之间及风险率函数之间的关系.最后,将Marshall-Olkin扩展重尾分布应用于实际数据中,并在拟合数据方面与原分布进行比较,表明扩展分布要优于原分布.     

求解大规模问题的谱共轭梯度法（英文）

共轭梯度法是求解大规模无约束优化问题的一类重要方法.由于共轭梯度法产生的搜索方向不一定是下降方向,为保证每次迭代方向都是下降方向,本文提出一种求解无约束优化问题的谱共轭梯度算法,该方法的每次搜索方向都是下降方向.当假设目标函数一致凸,且其梯度满足Lipschitz条件,线性搜索满足Wolfe条件时,讨论所设计算法的全局收敛性.     

基于截断Poisson分布的Marshall—Olkin拓展分布

采用复合分布的方法,将一个参数λ和一个已有分布组合成一个新的分布的方法,研究新分布与原分布之间的DFR的继承性和似然序关系.在原分布分别取为指数分布和正态分布时,分析其密度函数和危险率函数的等统计特征.最后,用一组数据进行实证研究,利用极大似然估计估计出参数,分别用指数扩展分布和指数分布拟合进行比较.     

基于分段损失分布法和Copula的银行操作风险集成度量

多个风险单元的集成度量是银行操作风险管理的关键步骤之一。立足于操作风险的“厚尾”、“截断”性,从分段损失分布法的视角出发,探讨操作风险集成度量的模式和数值方法。首先,引入两阶段损失分布法来拟合单个风险单元边际损失分布,用双截尾分布代替传统的完整分布来刻画“高频低损”损失数据的双截断特性,利用POT模型捕获“低频高损”事件的厚尾特性。再次,基于分段建模思路,对传统度量过程中边际分布为单一、完整分布的Copula模型进行了扩展,研究边际分布为分段分布、截尾分布条件下使用Copula函数集成度量操作风险的框架和步骤,并设计了Monte Carlo模拟算法。最后,以实证分析的形式验证所构建模型。通过对中国商业银行416个操作风险损失数据的实证分析,结果表明分段分布、截尾分布能对单个风险单元边际分布有更好的拟合效果,能减小由于分布选择不当而引发的模型风险。分段度量视角下Copula函数的引入能灵活处理多个操作风险单元间的相依结构,使风险度量结果更为合理。     

等几何分析的多重网格共轭梯度法

提高NURBS基函数阶数可以提高等几何分析的精度,同时也会降低多重网格迭代收敛速度.将共轭梯度法与多重网格方法相结合,提出了一种提高收敛速度的方法,该方法用共轭梯度法作为基础迭代算法,用多重网格进行预处理.对Poisson(泊松)方程分别用多重网格方法和多重网格共轭梯度法进行了求解,计算结果表明:等几何分析中采用高阶NURBS基函数处理三维问题时,多重网格共轭梯度法比多重网格法的收敛速度更快.     

Application of gradient descent method to the sedimentary grain-size distribution fitting

Existence of a least squares solution for a sum of several weighted normal functions is proved. The gradient descent (GD) method is used to fit the measured data (i.e. the laser grain-size distribution of the sediments) with a sum of three weighted lognormal functions. The numerical results indicate that the GD method is not only easy to operate but also could effectively optimize the parameters of the fitting function with the error decreasing steadily. Meanwhile the overall fitting results are satisfactory. As a new way of data fitting, the GD method could also be used to solve other optimization problems.     

一种模态傅里叶-支持向量机优化的取用水监测异常数据重构方法

提高数据的完备与真实性是水资源监控能力建设的关键。针对国家水资源监控能力建设项目实施以来其监测数据呈现出的异常特征,按照“先粗筛后精选”逻辑,并考虑取用水季节性周期波动的特点,提出采用拉依达准则-模态分解-傅里叶残差修正的水监测数据异常值识别方法,并根据粒子群优化最小二乘支持向量机模型实现对异常数据的重构恢复。通过对企业取用水数据的实例分析,结果表明分段式拉依达准则在其监测异常数据的粗筛中具有较好的适用性,利用傅里叶修正集合模态分解的监测数据序列可取得更佳的拟合效果,从而达到异常数据精选的目的;而粒子群优化最小二乘支持向量机模型对异常数据重构恢复的可信度高于普通最小二乘支持向量机及传统曲线拟合数据重构方法,即该类取用水监测异常数据重构方法可有助于进一步推进其监测数据对实际水资源状态的客观反映。     

B-spline curve fitting with invasive weed optimization

B-spline curves and surfaces are generally used in computer aided design (CAD), data visualization, virtual reality, surface modeling and many other fields. Especially, data fitting with B-splines is a challenging problem in reverse engineering. In addition to this, B-splines are the most preferred approximating curve because they are very flexible and have powerful mathematical properties and, can represent a large variety of shapes efficiently [1]. The selection of the knots in B-spline approximation has an important and considerable effect on the behavior of the final approximation. Recently, in literature, there has been a considerable attention paid to employing algorithms inspired by natural processes or events to solve optimization problems such as genetic algorithms, simulated annealing, ant colony optimization and particle swarm optimization. Invasive weed optimization (IWO) is a novel optimization method inspired from ecological events and is a phenomenon used in agriculture. In this paper, optimal knots are selected for B-spline curve fitting through invasive weed optimization method. Test functions which are selected from the literature are used to measure performance. Results are compared with other approaches used in B-spline curve fitting such as Lasso, particle swarm optimization, the improved clustering algorithm, genetic algorithms and artificial immune system. The experimental results illustrate that results from IWO are generally better than results from other methods.     

On the Gauss-Newton method for l1 orthogonal distance regression

The problem of fitting a curve or surface to data has many applications.There are also many fitting criteria which can be used, andone which is widely used in metrology, for example, is thatof minimizing the least squares norm of the orthogonal distancesfrom the data points to the curve or surface. The Gauss–Newtonmethod, in correct separated form, is a popular method for solvingthis problem. There is also interest in alternatives to leastsquares, and here we focus on the use of the l₁ norm, whichis traditionally regarded as important when the data containwild points. The effectiveness of the Gauss–Newton methodin this case is studied, with particular attention given tothe influence of zero distances. Different aspects of the computationare illustrated by consideration of two particular fitting problems.     

Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Effects Models With Nonnormal Random Effects

This article describes a simple computational method for obtaining the maximum likelihood estimates (MLE) in nonlinear mixed-effects models when the random effects are assumed to have a nonnormal distribution. Many computer programs for fitting nonlinear mixed-effects models, such as PROC NLMIXED in SAS, require that the random effects have a normal distribution. However, there is often interest in either fitting models with nonnormal random effects or assessing the sensitivity of inferences to departures from the normality assumption for the random effects. When the random effects are assumed to have a nonnormal distribution, we show how the probability integral transform can be used, in conjunction with standard statistical software for fitting nonlinear mixed-effects models (e.g., PROC NLMIXED in SAS), to obtain the MLEs. Specifically, the probability integral transform is used to transform a normal random effect to a nonnormal random effect. The method is illustrated using a gamma frailty model for clustered survival data and a beta-binomial model for clustered binary data. Finally, the results of a simulation study, examining the impact of misspecification of the distribution of the random effects, are presented.     

Robust penalized regression spline fitting with application to additive mixed modeling

An increasingly popular method for smoothing noisy data is penalized regression spline fitting. In this paper a new procedure is proposed for fitting robust penalized regression splines. This procedure is computationally fast, straightforward to implement, and can be paired with any smoothing parameter selection method. In addition, it can also be extended to other settings, such as additive mixed modeling. Both simulated and real data examples are used to illustrate the effectiveness of the procedure.     

一个通用的学生考试成绩分布的数学模型研究

学生考试成绩分布是教学研究的一个重要课题,可以从多个角度开展研究,如讨论成绩分布的基本规律、分析成绩分布的基本决定因素、研究如何获得准确合理的成绩分布、探讨根据成绩分布如何改进教学活动等.目前人们基本上是以定性研究为主,很少见到定量的学生成绩分布的研究.为了促进该领域研究的进一步开展,提出了一个定量的研究学生成绩分布的数学模型,该模型中唯一的假设是学生考试成绩取决于学习时间和学习效率.利用学生人数随着学习时间和学习效率分布的两个基本规律,获得了学生人数随考试成绩分布的基本规律.实践中两个基本规律可以通过问卷、测试、谈话或者借助于一定的理论分析等多种方式获得.基于分子运动论和高斯模型,获得了一种学生人数随着学习时间和学习效率分布的两个特殊规律,分析了其中参数的意义和影响,给出了一些很有意义的结论.该模型可以预测学生考试成绩的分布规律,获得学生学习状况的许多信息,有助于改进教学活动和提高学生学习积极性.这是一个非常初步的定量化研究,希望在未来的研究中不断改进和丰富.     

一个新的填充函数及其在数据拟合问题中的应用

填充函数法是求解全局优化问题的有效方法之一,针对无约束优化问题,提出一个新的连续可微的无参数填充函数,证明其相关性质并给出相应的算法,数值实验结果表明该算法是有效可行的。同时用此填充函数对切削温度实验数据这一拟合实例进行求解,与已有的最小二乘法和遗传算法的求解结果相比较,拟合效果较好。     

Dynamic programming approximation algorithms for the capacitated lot-sizing problem

This paper provides a new idea for approximating the inventory cost function to be used in a truncated dynamic program for solving the capacitated lot-sizing problem. The proposed method combines dynamic programming with regression, data fitting, and approximation techniques to estimate the inventory cost function at each stage of the dynamic program. The effectiveness of the proposed method is analyzed on various types of the capacitated lot-sizing problem instances with different cost and capacity characteristics. Computational results show that approximation approaches could significantly decrease the computational time required by the dynamic program and the integer program for solving different types of the capacitated lot-sizing problem instances. Furthermore, in most cases, the proposed approximate dynamic programming approaches can accurately capture the optimal solution of the problem with consistent computational performance over different instances.     

Least squares surface approximation to scattered data using multiquadratic functions

The paper documents an investigation into some methods for fitting surfaces to scattered data. The form of the fitting function is a multiquadratic function with the criteria for the fit being the least mean squared residual for the data points. The principal problem is the selection of knot points (or base points for the multiquadratic basis functions), although the selection of the multiquadric parameter also plays a nontrivial role in the process. We first describe a greedy algorithm for knot selection, and this procedure is used as an initial step in what follows. The minimization including knot locations and the multiquadric parameter is explored, with some unexpected results in terms of “near repeated” knots. This phenomenon is explored, and leads us to consider variable parameter values for the basis functions. Examples and results are given throughout.