确定星形分枝Markov链离子通道的转移速率

给出了Markov链中某状态集的生存时间和死亡时间的分布(均是混合指数分布),以及其分布的各阶导数与转移速率之间的约束关系.利用它们证明了:对于星形分枝Markov链离子通道,其全部转移速率能够通过中心状态及其相邻状态的生存时间和死亡时间的分布唯一确定,给出了相应的算法,并例证该算法的正确性和有效性.     

确定一类带环离子通道门控的转移速率

证明了Markov链中某状态集的生存时间和死亡时间的分布均是混合指数分布,并给出了此分布与转移速率之间的若干约束关系.利用它们证明了:对于文中的带环离子通道门控,四种情形的转移速率都能由其中两个开状态的生存时间和死亡时间分布唯一决定,并给出了相应的算法.而Wagner等指出只有一种情形是可以确定的.     

微通道中电渗流及微混合的离子浓度效应

电渗流广泛应用于微流控芯片中的流体输运与混合.该文提出了一种离子浓度梯度对电渗流及微混合产生影响的变量模型,采用有限元分析方法对微通道中电渗流及微混合的离子浓度效应进行了数值模拟,分别讨论了zeta电势、介电常数等对微通道内流场和浓度场的影响规律,定量分析了微混合效率.结果表明,当zeta电势和介电常数随浓度变化时,微通道中流场分布不均匀,离子分布不对称.当溶液浓度趋近1 mol/L时,溶液基本无法进入微通道.微混合效率随溶液间浓度差的增大而减小,而且浓度差越大越能在较短距离内到达充分混合.     

混合Weibull分布参数估计的ECM算法

混合威布尔分布是寿命数据分析中一个重要的统计模型.但是利用传统的统计方法,如矩估计、极大似然估计等估计模型的参数比较困难.应用ECM算法详细研究了混合威布尔分布在正常工作条件下,完全数据场合、Ⅰ-型截尾和Ⅱ-截尾场合的参数估计问题.数据模拟表明利用ECM算法来估计混合威布尔分布是一种有效的方法.     

定数截尾场合下混合指数威布尔分布的参数估计

混合指数威布尔分布是寿命数据分析中一个重要的统计模型.但是利用传统的矩法估计,极大似然估计等估计模型的参数比较困难.应用ECM算法,研究了混合指数威布尔分布在定数截尾数据场合下的参数估计问题,并以数值模拟验证用ECM算法来估计混合指数威布尔分布在定数截尾数据场合下的有效性.     

重尾数据的混合Erlang分布拟合方法

PH分布具有良好的解析性质,重尾数据的PH近似是随机模型分析中的重要课题.混合Erlang分布是一种常见的PH分布,本文利用EM方法给出了重尾数据的混合Erlang分布拟合算法,通过Matlab软件对两类常见重尾数据进行实验,结果令人满意.最后,文章对混合Erlang分布拟合算法的进一步改进做了总结.     

军事定向越野路径优化问题建模及混合蚁群算法求解

军事定向越野运动中存在点位多、分布散、时间紧、得分要求高等条件,为在规定时间内找到一条得分高的行进线路,找到衡量和分析运动成绩好坏的标准,文中对军事定向越野中的路径优化问题进行了深入分析,建立了混合整数规划模型,设计了一种混合蚁群算法。算法中,首先由改进蚁群算法找到初始解,然后再利用选择、交叉和变异算子进行解的优化,通过仿真实验和算法对比验证了混合蚁群算法的可行性和优越性。     

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。     

定数截尾下Burr-Ⅻ分布参数贝叶斯估计的混合Gibbs算法

利用混合Gibbs算法(Gibbs抽样与Metropolis算法混合)给出了定数截尾样本场合Burr-Ⅻ分布参数的贝叶斯估计,通过Monte-Carlo模拟,考查贝叶斯估计的均值、均方误差及参数的可信区间,并给出混合Gibbs抽样过程中相应参数的轨迹图、直方图及自相关系数图.结果表明:在定数截尾样本场合,用混合Gibbs算法求Burr-Ⅻ分布参数的贝叶斯估计得到了比较满意的结果,算法可行、稳定、有效.     

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.     

Statistical estimation of a mixture of Gaussian distributions

The paper is devoted to the problem of statistical estimation of a multivariate distribution density, which is a discrete mixture of Gaussian distributions. A heuristic approach is considered, based on the use of the EM algorithm and nonparametric density estimation with a sequential increase in the number of components of the mixture. Criteria for testing of model adequacy are discussed.     

The localisation of the discharge channel in a glow discharge

The localisation of the discharge channel under the conditions of a glow discharge in air, at different distances from the cathode and at various points in the cathode dark space for different voltages, has been studied by measuring the radial distribution of the light intensity in the discharge channel. It has been shown that the radial ion distribution which has a flat maximum at lower voltages changes over into an approximately Gaussian one as the discharge voltage is increased. In the former case the width of the flat maximum decreases as the ion-channel proceeds towards the cathode. At higher voltages (~ 6 kV.) the flat maximum type of distribution in the ion-channel progressively changes over into the Gaussian as the channel approaches the cathode.     

Model Misspecification: Finite Mixture or Homogeneous?

A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous non-mixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indistinguishable from homogenous non-normal densities. This paper illustrates what happens when the EM algorithm for normal mixtures is applied to a distribution that is a homogeneous non-mixture distribution. In particular, a population-based EM algorithm for finite mixtures is introduced and applied directly to density functions instead of sample data. The population-based EM algorithm is used to find finite mixture approximations to common homogeneous distributions. An example regarding the nature of a placebo response in drug treated depressed subjects is used to illustrate ideas.     

Using mixture density functions for modelling of wage distributions

This article explores the possibility of modeling the wage distribution using a mixture of density functions. We deal with this issue for a long time and we build on our earlier work. Classical models use the probability distribution such as normal, lognormal, Pareto, etc., but the results are not very good in the last years. Changing the parameters of a probability density over time has led to a degradation of such models and it was necessary to choose a different probability distribution. We were using the idea of mixtures of distributions (instead of using one classical density) in previous articles. We tried using a mixture of probability distributions (normal, lognormal and a mixture of Johnson’s distribution densities) in our models. The achieved results were very good. We used data from Czech Statistical Office covering the wages of the last 18 years in Czech Republic.     

Continuity of integrated density of states — independent randomness

In this paper we discuss the continuity properties of the integrated density of states for random models based on that of the single site distribution. Our results are valid for models with independent randomness with arbitrary free parts. In particular in the case of the Anderson type models (with stationary, growing, decaying randomness) on the ν dimensional lattice, with or without periodic and almost periodic backgrounds, we show that if the single site distribution is uniformly α-Hölder continuous, 0 < α ≤ 1, then the density of states is also uniformly α-Hölder continuous.     

Two New Mixture Models Related to the Inverse Gaussian Distribution

This article presents a new family of logarithmic distributions to be called the sinh mixture inverse Gaussian model and its associated life distribution referred as the extended mixture inverse Gaussian model. Specifically, the density, distribution function, and moments are developed for the sinh mixture inverse Gaussian distribution. Next, the extended mixture inverse Gaussian distribution is characterized. A graphical analysis of the densities of the new models is also provided. In addition, a lifetime analysis is presented for the extended mixture inverse Gaussian distribution. Finally, an example with a real data set is given to illustrate the methodology, which indicates that the new models result in a better fit to the data than some other well-known distributions.     

基于混合密度网络模型拟合收益统计分布与计算Expected Shortfall

基于混合密度网络模型估计金融时间序列的时变条件密件,提出数值模拟方法计算ExpectedShortfall。对香港恒生指数的实证研究表明,混合密度网络可以有效地描述收益的经验分布统计特征和波动规律,模型评估指标反映出预测效果良好,Value-at-R isk的预测精度在高端分位点表现较好,且可有效计算Expected shortfall指标,是金融市场风险测量的有效方法。     

Normal mixture quasi maximum likelihood estimation for non-stationary TGARCH(1,1) models

Although quasi maximum likelihood estimator based on Gaussian density (G-QMLE) is widely used to estimate GARCH-type models, it does not perform successfully when error distribution is either skewed or leptokurtic. This paper proposes normal mixture quasi-maximum likelihood estimator (NM-QMLE) for non-stationary TGARCH(1,1) models. We show that, under mild regular conditions, there is no consistent estimator for the intercept, and the proposed estimator for any other parameter is consistent.     

A hybrid Pareto model for asymmetric fat-tailed data: the univariate case

Density estimators that can adapt to asymmetric heavy tails are required in many applications such as finance and insurance. Extreme value theory (EVT) has developed principled methods based on asymptotic results to estimate the tails of most distributions. However, the finite sample approximation might introduce a severe bias in many cases. Moreover, the full range of the distribution is often needed, not only the tail area. On the other hand, non-parametric methods, while being powerful where data are abundant, fail to extrapolate properly in the tail area. We put forward a non-parametric density estimator that brings together the strengths of non-parametric density estimation and of EVT. A hybrid Pareto distribution that can be used in a mixture model is proposed to extend the generalized Pareto (GP) to the whole real axis. Experiments on simulated data show the following. On one hand, the mixture of hybrid Paretos converges faster in terms of log-likelihood and provides good estimates of the tail of the distributions when compared with other density estimators including the GP distribution. On the other hand, the mixture of hybrid Paretos offers an alternate way to estimate the tail index which is comparable to the one estimated with the standard GP methodology. The mixture of hybrids is also evaluated on the Danish fire insurance data set.      

A recursive approach to mortality-linked derivative pricing

In this paper, we develop a recursive method to derive an exact numerical and nearly analytical representation of the Laplace transform of the transition density function with respect to the time variable for time-homogeneous diffusion processes. We further apply this recursion algorithm to the pricing of mortality-linked derivatives. Given an arbitrary stochastic future lifetime T, the probability distribution function of the present value of a cash flow depending on T can be approximated by a mixture of exponentials, based on Jacobi polynomial expansions. In case of mortality-linked derivative pricing, the required Laplace inversion can be avoided by introducing this mixture of exponentials as an approximation of the distribution of the survival time T in the recursion scheme. This approximation significantly improves the efficiency of the algorithm.