广义Pareto分布的广义有偏概率加权矩估计方法

广义Pareto分布(GPD)是统计分析中一个极为重要的分布,被广泛应用于金融、保险、水文及气象等领域.传统的参数估计方法如极大似然估计、矩估计及概率加权矩估计方法等已被广泛应用,但使用中存在一定的局限性.虽然提出很多改进方法如广义概率加权矩估计、L矩和LH矩法等,但都是研究完全样本的估计问题,而在水文及气象等应用领域常出现截尾样本.本文基于概率加权矩理论,利用截尾样本对三参数GPD提出一种应用范围广且简单易行的参数估计方法,可有效减弱异常值的影响.首先求解出具有较高精度的形状参数的参数估计,其次得出位置参数及尺度参数的参数估计.通过Monte Carlo模拟说明该方法估计精度较高.     

非参数方法下条件矩限制回归模型的广义矩估计

本文讨论条件矩限制回归模型的参数估计.使用非参数估计方法给出条件密度和条件均值的估计,在此基础上给出参数的广义矩估计.进一步讨论了估计的渐近正态性.     

Copula函数中参数的矩估计方法

Copula函数是将多维随机变量的联合分布和其边缘分布连接起来的一种函数.关于Copula函数的理论和应用已有不同深度的研究,特别是Copula函数中未知参数的估计问题.本文研究了Gumbel Copula函数的参数估计,提出了矩估计和近似矩估计两种方法,分别得到了未知参数的估计结果,并通过模拟研究对这两种方法进行了比较,结果显示矩估计方法更为合理.     

基于矩和L矩的三参数Ⅰ型广义Logistic分布的参数估计

广义Logistic分布是一族重要的分布,被广泛地应用于生物学、医学、金融管理学,以及气象、水文、地质等领域.迄今为止,对于Logistic分布,统计学者已给出诸多的统计推断理论和方法,以及众多应用成果.令人遗憾的是,对应用非常广泛的广义Logistic分布,特别是具有位置、刻度和形状参数的三参数Ⅰ型广义Logistic分布的研究还有待深入,该分布的应用还需进—步开发和利用.本文利用矩法和L矩法讨论三参数Ⅰ型广义Logistic分布的参数估计,给出两种估计形式下参数的估计方程;证明了在一定的条件下,估计方程的解存在、唯一,且渐近正态地相合于真实参数的结论.通过计算机模拟,比较不同参数、不同样本容量下两种估计的估计效果.     

混合双参数广义Pareto分布的参数估计

Pareto分布族因其厚尾特点,在金融分析、寿命分析中都是非常重要的统计模型.但是对于混合双参广义Pareto分布,在模型参数估计时,传统的矩法估计和极大似然估计在理论上可以实现,实践时比较困难.本文应用EM算法之ECM算法,研究了混合广义Pareto分布在完全数据场合下的参数估计问题,并模拟说明EM算法来估计混合广义Pareto分布是一种容易实现又非常有效的方法.     

多元广义泊松分布的参数估计与诊断

广义泊松分布是普通泊松分布的自然推广,克服均值与方差相等的局限性.在计数数据中,常常会有多变量的情形,比如保险保单定价.因此文章考虑多元广义泊松分布的参数估计和假设检验问题,针对共协方差多元广义泊松模型提出两种参数估计的方法,矩估计方法和极大似然估计方法,并比较两种方法的优劣性.文章就多元广义泊松分布的假设检验问题,主要探讨了其退化检验及独立性检验,由于参数及变量较多,运用似然比检验方法构造服从卡方分布的检验统计量.最后,运用多元广义泊松理论分析不同地区森林发生火灾的次数,首先用文中提到的检验方法诊断数据是否可以用多元广义泊松分布,其次进行参数估计及实际问题的分析解释.     

纵向数据广义部分线性模型的惩罚GMM估计

对纵向数据的部分线性模型,通常的做法是用样条方法或者核方法逼近非参数部分,然后再用广义估计方程的估计方法去估计参数部分.本文使用P-样条拟合非参数函数,对不同的矩条件用不同的广义矩方法对模型的参数和非参数进行估计,并且给出了估计量的大样本性质;并用计算机模拟和实例证明了当模型中存在不同的矩条件时,采用不同的惩罚广义矩方法可以显著地提高估计精度.     

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.     

三参数Ⅰ型广义Logistic分布参数的一类改进估计

广义Logistic分布族在生物、医学、金融管理,以及气象、水文、地质等领域有重要的应用。迄今为止,对此分布族的研究已取得了一系列重要成果;令人遗憾的是,关于三参数I型广义Logistic分布的研究还很不深入。本文利用矩法首先讨论三参数I型广义Logistic分布形状参数的估计,然后利用线性回归分析方法讨论分布的位置参数和刻度参数的估计,改进矩估计。本文所给出的分布参数的估计方法简单、有效;证明了在一定的条件下,本文给出的估计量存在、唯一,且模拟显示:估计量在中小样本情形下,一致优于分布参数的矩估计和L矩估计;特别是在样本容量n介于20和30之间时,估计量有更小的估计偏差和方差。估计方法简单、实用且有效。     

离散抽样OU-复合Poisson过程的参数矩估计

本文研究基于离散观测的正复合Poisson过程驱动OU型过程的参数估计. 通过矩估计给出了过程平稳分布参数的估计量, 并得到了估计量的相合性和渐近正态性. 进一步, 将矩估计的方法和结论推广到叠加过程的情况.     

正态分布和瑞利分布混合情形下的参数估计及分类问题

学者往往用单一的分布模拟和拟合杂波,如正态分布、瑞利分布和威布尔分布等。然而在实际中,雷达杂波由多种类型的杂波组成,单一分布通常不能精确刻画雷达杂波规律,因此,应用混合分布模型对雷达杂波数据建模更准确。本文考虑用正态分布和瑞利分布的混合分布拟合杂波,并应用矩估计方法和基于EM算法的极大似然估计方法估计模型参数,最后,应用最大后验概率分类准则验证2种估计方法的分类准确率。通过数据模拟,得出极大似然估计的效果和分类准确率都要优于矩估计的估计效果和分类准确率。     

负二项分布两种参数估计及其比较

负二项分布在昆虫空间分布中有重要应用,其参数常用矩法或零频率法来估计。本文讨论两种估计的性质,得到大样本情形下,零频率较大时,零频率估计较矩估计有更高精度的结论;通过计算机模拟,验证结论。     

Spectral analysis of large block random matrices with rectangular blocks

In this paper, we study the spectral properties of the large block random matrices when the blocks are general rectangular matrices. Under some moment assumptions of the underlying distributions, we prove the existence of the limiting spectral distribution (LSD) of the block random matrices. Further, we determine the Stieltjes transform of the LSD under the same moment conditions by demonstrating that it is the same as in the case where the underlying distributions are Gaussian.     

基于最大惩罚似然的高斯混合模型无监督分类研究

本文提出了一个基于高斯混合模型的无监督分类算法. 考虑到利用EM算法求解高斯混合模型的参数参数估计问题容易陷入局部最优解, 我们引入逆Wishart分布来代替传统的Jeffery先验. 几个实验数据的结果表明, 采用该方法估计无监督分类的成分数, 无论是估计的正确率, 还是运算速度, 都有较大提高.     

Distributionally robust parameter identification of a time-delay dynamical system with stochastic measurements

In this paper, we consider a parameter identification problem involving a time-delay dynamical system, in which the measured data are stochastic variable. However, the probability distribution of this stochastic variable is not available and the only information we have is its first moment. This problem is formulated as a distributionally robust parameter identification problem governed by a time-delay dynamical system. Using duality theory of linear optimization in a probability space, the distributionally robust parameter identification problem, which is a bi-level optimization problem, is transformed into a single-level optimization problem with a semi-infinite constraint. By applying problem transformation and smoothing techniques, the semi-infinite constraint is approximated by a smooth constraint and the convergence of the smooth approximation method is established. Then, the gradients of the cost and constraint functions with respect to time-delay and parameters are derived. On this basis, a gradient-based optimization method for solving the transformed problem is developed. Finally, we present an example, arising in practical fermentation process, to illustrate the applicability of the proposed method.     

Classical and Bayesian inference in two parameter exponential distribution with randomly censored data

This paper deal with the classical and Bayesian estimation for two parameter exponential distribution having scale and location parameters with randomly censored data. The censoring time is also assumed to follow a two parameter exponential distribution with different scale but same location parameter. The main stress is on the location parameter in this paper. This parameter has not yet been studied with random censoring in literature. Fitting and using exponential distribution on the range \((0, \infty )\), specially when the minimum observation in the data set is significantly large, will give estimates far from accurate. First we obtain the maximum likelihood estimates of the unknown parameters with their variances and asymptotic confidence intervals. Some other classical methods of estimation such as method of moment, L-moments and least squares are also employed. Next, we discuss the Bayesian estimation of the unknown parameters using Gibbs sampling procedures under generalized entropy loss function with inverted gamma priors and Highest Posterior Density credible intervals. We also consider some reliability and experimental characteristics and their estimates. A Monte Carlo simulation study is performed to compare the proposed estimates. Two real data examples are given to illustrate the importance of the location parameter.     

Estimation in threshold autoregressive models with correlated innovations

Large sample statistical analysis of threshold autoregressive models is usually based on the assumption that the underlying driving noise is uncorrelated. In this paper, we consider a model, driven by Gaussian noise with geometric correlation tail and derive a complete characterization of the asymptotic distribution for the Bayes estimator of the threshold parameter.     

Borgonovo moment independent global sensitivity analysis by Gaussian radial basis function meta-model

Moment independent sensitivity index is widely concerned and used since it can reflect the influence of model input uncertainty on the entire distribution of model output instead of a specific moment. In this paper, a novel analytical expression to estimate the Borgonovo moment independent sensitivity index is derived by use of the Gaussian radial basis function and the Edgeworth expansion. Firstly, the analytical expressions of the unconditional and conditional first four-order moments are established by the training points and the widths of the Gaussian radial basis function. Secondly, the Edgeworth expansion is used to express the unconditional and conditional probability density functions of model output by the unconditional and conditional first four-order moments, respectively. Finally, the index can be readily computed by measuring the shifts between the obtained unconditional and conditional probability density functions of model output, where this process doesn't need any extra calls of model evaluation. The computational cost of the proposed method is independent of the dimensionality of model inputs and it only depends on the training points and the widths which are involved in the Gaussian radial basis function meta-model. Results of several case studies demonstrate the effectiveness of the proposed method.     

Distribution modeling for reliability analysis: Impact of multiple dependences and probability model selection

Reliability analysis requires modeling of joint probability distribution of uncertain parameters, which can be a challenge since the random variables representing the parameter uncertainties may be correlated. For convenience, a Gaussian data dependence is commonly assumed for correlated random variables. This paper first investigates the effect of multidimensional non-Gaussian data dependences underlying the multivariate probability distribution on reliability results. Using different bivariate copulas in a vine structure, various data dependences can be modeled. The associated copula parameters are identified from available statistical information by moment matching techniques. After the development of the vine copula model for representing the multivariate probability distribution, the reliability involving correlated random variables is evaluated based on the Rosenblatt transformation. The impact of data dependence is significant because a large deviation in failure probability is observed, which emphasizes the need for accurate dependence characterization. A practical method for dependence modeling based on limited data is thus provided. The result demonstrates that the non-Gaussian data dependences can be real in practice, and the reliability can be biased if the Gaussian dependence is used inappropriately. Moreover, the effect of conditioning order on reliability should not be overlooked except that the vine structure contains only one type of copula.     

Estimation of coefficient of variation in a weighted inverse Gaussian model

The coefficient of variation is an important parameter in many physical, biological and medical sciences. In this paper we study the estimation of the square of the coefficient of variation in a weighted inverse Gaussian model which is a mixture of the inverse Gaussian and the length biased inverse Gaussian distribution. This represents a rich family of distributions for different values of the mixing parameter and can be used for modelling various life testing situations. The maximum likelihood as well as the Bayes estimates of the parameters are obtained. These estimates are used to derive the estimates of the square of the coefficient of variation of the model under study. Several important data sets are analysed to illustrate the results. © 1996 John Wiley & Sons, Ltd.