t分布的代表点及其在统计模拟中的应用

本文首先讨论了在均方误最小意义下学生氏tn分布代表点,利用方开泰、贺曙东的算法找出代表点,证明了当n≥3时,t_n分布总体下算法的收敛性,代表点的存在性及其唯一性,并从t_n分布角度研究了代表点在统计模拟方面的应用。传统的蒙特卡罗方法和自助法及重抽样方法对随机样本进行抽样,是统计模拟方法的基础.Fang,Zhou,Wang讨论了一元正态分布的代表点在统计模拟中的应用并且首次提出用代表点代替独立同分布的随机样本,构造一个离散的近似总体,通过对近似总体重复抽样来进行统计推断.这是—个新思想.本文继续探讨这个问题,文中统计推断主要有两部分:经典估计和稳健估计.经典估计主要集中考虑参数点估计(均值、方差、偏度和峰度);稳健估计主要考虑简单的位置参数(中位数和均值)和尺度参数(中位数绝对偏差和四分位距).我们的结果再次验证,代表点方法可以明显地提高统计估计量的精确度以及收敛速度.     

有缺失数据的正态母体参数的后验分布及其抽样算法

在缺失数据机制是可忽略的、先验分布是逆矩阵Γ分布的假设下,利用矩阵的cholesky分解和变量替换方法,本文导出了有单调缺失数据结构的正态分布参数的后验分布形式.进-步用后验分布的组成特点,构造了单调缺失数据结构的正态分布的协方差矩阵和均值后验分布的抽样算法.     

对数正态分布和Weibull分布变差系数的抽样验证方案

本文在参数的极大似然估计的基础上，给出了对数正态分布和Ｗｅｉｂｕｌ分布变差系数的抽样验证方案．     

漫谈中心极限定理

一个奇特的现象 不知道你是否了解这样一个奇特现象,即不管总体是离散分布或连续分布,是正态分布或非正态分布,从中经重复抽样所得的样本均值(x-)的直方图总是呈现"中间高,两边低,左右基本对称"的正态分布形状,且样本量n愈大此种近似程度愈好,就像下面这个例子所表露的一样.     

对数正态分布和Weibull分布变差系数的抽样验证方案

本文在参数的极大似然估计的基础上．给出了对数正态分布和Weibull分布变差系数的抽样验证方案．     

对数稳定分布加速寿命试验的贝叶斯分析

对数稳定分布与对数正态分布相比具有厚尾等良好的特性,由于没有显式的密度函数,其参数估计有一定的困难.运用Gibbs抽样方法给出了对数稳定分布的Bayes参数估计,与对数正态分布参数估计进行了比较,并应用到恒加寿命试验数据的分析中.模拟研究说明了该方法的有效性,对数稳定分布作为新的寿命分布是合适的.     

P-范分布及其抽样分布

本文构造了n维P-范分布的密度函数，使拉普拉斯分布、正态分布、均匀分布与退化分布均为一维P-范分布的特例。然后，定义了三个与P-范分布有密切关系的抽样分布，并给出了密度函数。     

基于M-H抽样的贝叶斯非对称厚尾GARCH模型研究

针对非对称厚尾GARCH模型参数的预选分布很难确定的问题。对模型参数空间进行数据扩张,把模型中的厚尾残差分布表示成正态分布和逆伽玛分布的混合分布,然后通过对参数的后验条件分布进行变换获得参数的预选分布,从而利用M-H抽样实现了非对称厚尾GARCH模型的贝叶斯分析。中国原油收益率波动的实证研究发现中国原油收益率的波动具有高峰厚尾性但不存在"杠杆效应",样本内的预测评价发现基于M-H抽样的贝叶斯方法优于极大似然方法,说明了M-H抽样方案设计的有效性。     

一类非正态总体的抽样检验方案

目前对抽样方案已有较多的研究，且在各个领域有广泛的应用。但是这些研究大多是建立在产品特征服从正态分布的基础上，对其它分布研究较少。本文就产品特征服从韦布分布的情况，建立了一类计量抽样检验方案，并求出其OC函数。本文主要讨论以均值衡量批质量的情形。     

基于推广多因子模型的资产动态配置研究——以上证A股为例

考虑投资者关于市场的观点,并用椭球分布替代正态分布作为资产收益和投资者观点分布导出推广的多因子模型(EMFM).采集2015年1月5日到2018年9月11日上证A股中分列于11个行业共828只股票的日收益率及其相关指标,采用5个系统风险因子,在3种常用风险指标下应用EMFM对该高数据进行动态建模并做资产配置分析.实证结果显示:从回溯期内的收益表现来看,使用自由度为3的多维t分布,一种特殊的椭球分布,即便合并简单的绝对观点的EMFM在SD风险指标下的组合都优于马科维兹组合的情形,同时也优于其使用VaR和CVaR的情形,且所有组合都优于上证指数的情形.     

On interval estimation of the coefficient of variation for the three-parameter Weibull,lognormal and gamma distribution: A simulation-based approach

The coefficient of variation (CV) of a population is defined as the ratio of the population standard deviation to the population mean. It is regarded as a measure of stability or uncertainty, and can indicate the relative dispersion of data in the population to the population mean. CV is a dimensionless measure of scatter or dispersion and is readily interpretable, as opposed to other commonly used measures such as standard deviation, mean absolute deviation or error factor, which are only interpretable for the lognormal distribution. CV is often estimated by the ratio of the sample standard deviation to the sample mean, called the sample CV. Even for the normal distribution, the exact distribution of the sample CV is difficult to obtain, and hence it is difficult to draw inferences regarding the population CV in the frequentist frame. Different methods of estimating the sample standard deviation as well as the sample mean result in different shapes of the sampling distribution of the sample CV, from which inferences about the population CV can be made. In this paper we propose a simulation-based Bayesian approach to tackle this problem. A set of real data is used to generate the sampling distribution of the CV under the assumption that the data follow the three-parameter Gamma distribution. A probability interval is then constructed. The method also applies easily to lognormal and Weibull distributions.     

柯西分布的参数估计

研究了柯西分布的参数估计问题,给出了位置参数的最小一乘估计和尺度参数的低阶矩估计.证明了柯西分布位置参数的最小一乘估计具有渐近无偏性与强相合性;尺度参数的低阶矩估计具有强相合性.     

On the joint distribution of studentized order statistics

In this paper, the joint distribution of some special linear combinations of the (internally) studentized order statistics are derived for both normal and exponential populations; the exact relationship between their pdf's is also obtained. The exact sampling distributions of studentized extreme deviation statistic, which has been proposed by Pearson and Chandra Sekar (1936,Biometrika,28, 308–320), are derived for these two populations. An application to the most powerful location and scale invariant test is discussed briefly.     

Robust errors-in-variables linear regression via Laplace distribution

Robust estimation procedures for linear and mixture linear errors-in-variables regression models are proposed based on the relationship between the least absolute deviation criterion and maximum likelihood estimation in a Laplace distribution. The finite sample performance of the proposed procedures is evaluated by simulation studies.     

Stability of digital force control in the presence of damping

In the plain of the proportional gain and the ratio of the natural and sampling frequencies, the stability charts of digital force control are constructed for one degree of freedom mechanical models with large variety of viscous damping parameters. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Accuracy of the bootstrap approximation

Summary The sampling distribution of several commonly occurring statistics are known to be closer to the corresponding bootstrap distribution than the normal distribution, under some conditions on the moments and the smoothness of the population distribution. These conditional approximations are suggestive of the unconditional ones considered in this paper, though one cannot be derived from the other by elementary methods. In this paper, probabilistic bounds are provided for the deviation of the sampling distribution from the bootstrap distribution. The rate of convergence to one, of the probability that the bootstrap approximation outperforms the normal approximation, is obtained. These rates can be applied to obtain theL ^p bounds of Bhattacharya and Qumsiyeh (1989) under weaker conditions. The results apply to studentized versions of functions of multivariate means and thus cover a wide class of common statistics. As a consequence we also obtain approximations to percentiles of studentized means and their appropriate modifications. The results indicate the accuracy of the bootstrap confidence intervals both in terms of the actual coverage probability achieved and also the limits of the confidence interval.Research supported in part by NSA Grant MDA 904-90-H-1001     

A new risk criterion in fuzzy environment and its application

Since the observed values of security returns in real-world problems are sometimes imprecise or vague, an increasing effort in research is devoted to study the properties of risk measures in fuzzy portfolio optimization problems. In this paper, a new risk measure is suggested to gauge the risk resulted from fuzzy uncertainty. For this purpose, the absolute deviation and absolute semi-deviation are first defined for fuzzy variable by nonlinear fuzzy integrals. To compute effectively the absolute semi-deviations of single fuzzy variable as well as its functions, this paper discusses the methods of computing the absolute semi-deviation by classical Lebesgue–Stieltjes (L–S) integral. After that, several useful absolute deviation and absolute semi-deviation formulas are established for common triangular, trapezoidal and normal fuzzy variables. Applying the absolute semi-deviation as a new risk measure in portfolio optimization, three classes of fuzzy portfolio optimization models are developed by combining the absolute semi-deviation with expected value operator and credibility measure. Based on the analytical representation of absolute semi-deviations, the established fuzzy portfolio selection models can be turned into their equivalent piecewise linear or fractional programming problems. Since the absolute semi-deviation is a piecewise fractional function and pseudo-convex on the feasible subregions of deterministic programming models, we take advantage of the structural characteristics to design a domain decomposition method to separate a deterministic programming problem into three convex subproblems, which can be solved by conventional solution methods or general-purpose software. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness of the solution method.     

基于分形B-S定价模型的认购权证价格行为实证分析

针对证券收益率呈现"尖峰厚尾"的分布特征,在分析传统B-S权证定价模型的不足基础上,本文提出了基于分形理论的B-S权证定价模型,并利用分形B-S权证定价模型和传统B-S模型分析认购权证价格变化的行为。实证结果发现,两种模型的理论价格均低估了市场价格,且低估的程度具有显著统计性,其中以分形B-S模型评价结果最接近市场价格,评价绩效好。探讨影响分形B-S权证模型理论价格与市场价格差异的主要因素,结果发现距到期日时间的长短、价内外程度以及流动性在解释价差程度上具有统计的显著性。     

席位分配的平均公平度方法

定义个体相对于总体的公平程度,即个体公平度与总体绝对公平度的比值,当比值趋于1时,就说明分配方案使该个体满意.利用方差的概念定义平均公平度,使个体公平程度相对于总体的公平程度的差距最小,等价于每一个个体公平度都很接近,并且趋于1,每个个体的公平程度达到最大,此时座位分配最为公平.