基于EM算法隐Markov模型参数估计

描述最大似然参数估计问题,介绍如何用EM算法求解最大似然参数估计.首先给出EM算法的抽象形式,然后介绍EM算法的一个应用:求隐Markov模型中的参数估计.用EM算法推导出隐Markov模型中参数的迭代公式.     

2PL模型的EM缺失数据处理方法研究

项目反应理论(IRT)模型是教育统计与测量中一种十分重要的模型,它包含项目参数和能力参数.目前一种常用的估计IRT模型项目参数的方法是由Woodruff和Hanson(1997)应用EM算法给出的,它用于完全反应数据,而把能力参数看作缺失数据.本文将Woodruff的方法推广到处理缺失反应的情况,基本思想是把能力参数和缺失反应均看作缺失数据,再运用EM算法估计参数.通过模拟研究,在不同被试人数和不同缺失比例的情况下,本文比较了我们给出的方法和BILOG-MG软件的缺失数据处理方法的参数估计效果.结果表明,在大多数情况下,本文提出的方法能得到更好的估计.     

基于分组数据的Weibull分布的参数估计

介绍了一种对基于分组数据的Weibull分布进行参数估计的方法．所得估计具有良好的收敛性，同时模拟结果也表明这种方法的可行性．     

成败型数据下Logistic分布的参数估计

使用EM算法 ,在成败型数据下 ,对Logistic分布的参数进行估计 ,得到了估计量所满足的非线性方程组     

系统发育树构建中用EM算法进行参数估计

系统发育学研究物种之间的进化关系,其核苷酸替代模型通常假设序列进化没有数据的缺损和删失,而现实中这个假设条件是很难满足的.针对这种事实,本文将运用EM算法对存在插入或缺失但序列长度假设不变的观测序列构建系统发育树进行参数估计,为含缺损数据序列构建良好的系统发育树作铺垫.重点在于运用EM算法做Jukes-Cantor模型、Kimura模型下含缺损数据的DNA序列构建有根树或无根树最佳分枝长度等的参数估计.     

一种计算EM步长的方法

本文使用ＥＭ算法解决某些完全数据下的参数估计问题，使用Ｆｉｓｈｅｒ公式计算ＥＭ步长，避免了求解非线性方程和计算条件期望。对区间型数据，成败型数据和重复测量模型，使用上述方法得到ＥＭ步长的计算公式。     

变环境情形下Weibull分布分组数据的参数估计

本文研究了变环境情形下Weibull分布分组数据可靠性估计的参数估计问题。给出一种基于EM算法的变环境分组数据Weibull分布参数估计方法,所得估计量具有良好的收敛性,模拟结果表明方法的实践可用性。     

Monte Carlo EM加速算法

EM算法是近年来常用的求后验众数的估计的一种数据增广算法, 但由于求出其E步中积分的显示表达式有时很困难, 甚至不可能, 限制了其应用的广泛性. 而Monte Carlo EM算法很好地解决了这个问题, 将EM算法中E步的积分用Monte Carlo模拟来有效实现, 使其适用性大大增强. 但无论是EM算法, 还是Monte Carlo EM算法, 其收敛速度都是线性的, 被缺损信息的倒数所控制, 当缺损数据的比例很高时, 收敛速度就非常缓慢. 而Newton-Raphson算法在后验众数的附近具有二次收敛速率. 本文提出Monte Carlo EM加速算法, 将Monte Carlo EM算法与Newton-Raphson算法结合, 既使得EM算法中的E步用Monte Carlo模拟得以实现, 又证明了该算法在后验众数附近具有二次收敛速度. 从而使其保留了Monte Carlo EM算法的优点, 并改进了Monte Carlo EM算法的收敛速度. 本文通过数值例子, 将Monte Carlo EM加速算法的结果与EM算法、Monte Carlo EM算法的结果进行比较, 进一步说明了Monte Carlo EM加速算法的优良性.     

Ⅰ型逐阶删失数据下基于EM算法的Weibull参数估计

研究了Ⅰ型逐阶删失数据下基于EM算法的Weibull参数估计,模拟产生不同Weibull参数组合和删失计划下的Ⅰ型逐阶删失数据,应用基于,EM算法的极大似然估计方法得到参数的估计值,并与数值方法得到的极大似然估计值进行对比,说明EM算法的估计效果.对73名肾脏移植患者生存数据进行实例分析,验证了基于EM算法的参数估计方法的可行性.     

缺失数据下含几何分布的对数线性模型的EM算法

本文研究缺失数据下对数线性模型参数的极大似然估计问题.通过Monte-Carlo EM算法去拟合所提出的模型.其中,在期望步中利用Metropolis-Hastings算法产生一个缺失数据的样本,在最大化步中利用Newton-Raphson迭代使似然函数最大化.最后,利用观测数据的Fisher信息得到参数极大似然估计的渐近方差和标准误差.     

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??In this paper, we estimate parameters of gamma life distribution and normal life distribution by EM algorithm based on Type-II hybrid censored data. The covariance matrices are derived as well. Some numerical examples are also presented for illustration.     

收益管理中非正态无约束估计的EM算法研究

对于收益管理中产品各"预售提前期间隔"的无约束需求,已有的无约束估计研究所采用的EM算法均假设其服从正态分布,这不完全符合收益管理实践中的需求数据特征。本文对收益管理无约束需求分布问题进行了多角度探讨,扩展了原有假设,建立了基于伽玛、威布尔、指数和泊松分布的EM算法,并改进了无失效数据情况下的算法应用。最后,通过数值算例说明了本文提出的基于非正态分布的EM算法简单易行,在结论部分对其相关应用提出了建议。     

A Component-Wise EM Algorithm for Mixtures

Maximum likelihood estimation in finite mixture distributions is typically approached as an incomplete data problem to allow application of the expectation-maximization (EM) algorithm. In its general formulation, the EM algorithm involves the notion of a complete data space, in which the observed measurements and incomplete data are embedded. An advantage is that many difficult estimation problems are facilitated when viewed in this way. One drawback is that the simultaneous update used by standard EM requires overly informative complete data spaces, which leads to slow convergence in some situations. In the incomplete data context, it has been shown that the use of less informative complete data spaces, or equivalently smaller missing data spaces, can lead to faster convergence without sacrifying simplicity. However, in the mixture case, little progress has been made in speeding up EM. In this article we propose a component-wise EM for mixtures. It uses, at each iteration, the smallest admissible missing data space by intrinsically decoupling the parameter updates. Monotonicity is maintained, although the estimated proportions may not sum to one during the course of the iteration. However, we prove that the mixing proportions will satisfy this constraint upon convergence. Our proof of convergence relies on the interpretation of our procedure as a proximal point algorithm. For performance comparison, we consider standard EM as well as two other algorithms based on missing data space reduction, namely the SAGE and AECME algorithms. We provide adaptations of these general procedures to the mixture case. We also consider the ECME algorithm, which is not a data augmentation scheme but still aims at accelerating EM. Our numerical experiments illustrate the advantages of the component-wise EM algorithm relative to these other methods.     

ML Estimation of the MultivariatetDistribution and the EM Algorithm

Maximum likelihood estimation of the multivariatetdistribution, especially with unknown degrees of freedom, has been an interesting topic in the development of the EM algorithm. After a brief review of the EM algorithm and its application to finding the maximum likelihood estimates of the parameters of thetdistribution, this paper provides new versions of the ECME algorithm for maximum likelihood estimation of the multivariatetdistribution from data with possibly missing values. The results show that the new versions of the ECME algorithm converge faster than the previous procedures. Most important, the idea of this new implementation is quite general and useful for the development of the EM algorithm. Comparisons of different methods based on two datasets are presented.     

An Interval Analysis Approach to the EM Algorithm

Abstract

The EM algorithm is widely used in incomplete-data problems (and some complete-data problems) for parameter estimation. One limitation of the EM algorithm is that, upon termination, it is not always near a global optimum. As reported by Wu (1982), when several stationary points exist, convergence to a particular stationary point depends on the choice of starting point. Furthermore, convergence to a saddle point or local minimum is also possible. In the EM algorithm, although the log-likelihood is unknown, an interval containing the gradient of the EM q function can be computed at individual points using interval analysis methods. By using interval analysis to enclose the gradient of the EM q function (and, consequently, the log-likelihood), an algorithm is developed that is able to locate all stationary points of the log-likelihood within any designated region of the parameter space. The algorithm is applied to several examples. In one example involving the t distribution, the algorithm successfully locates (all) seven stationary points of the log-likelihood.     

Beta-Binomial模型基于EM算法的应用研究

在很多问题中,Beta-Binomial模型有着较为广泛的应用,本文基于EM算法研究了Beta-Binomial模型的参数估计问题,并把它应用于实际的案例中.结果表明我们提出的方法计算方便,对具体问题的解释更具合理性、科学性.     

An EM Algorithm for Batch Markovian Arrival Processes and its Comparison to a Simpler Estimation Procedure

Although the concept of Batch Markovian Arrival Processes (BMAPs) has gained widespread use in stochastic modelling of communication systems and other application areas, there are few statistical methods of parameter estimation proposed yet. However, in order to practically use BMAPs for modelling, statistical model fitting from empirical time series is an essential task. The present paper contains a specification of the classical EM algorithm for MAPs and BMAPs as well as a performance comparison to the computationally simpler estimation procedure recently proposed by Breuer and Gilbert. Furthermore, it is shown how to adapt the latter to become an estimator for hidden Markov models.