q-Deformed Gelfand-Dickey系列规范变换的行列式表示

Gelfand-Dickey系列是十分重要的可积系统.本文用规范变换来讨论q-Deformed GelfandDickey系列的解,得到了由两种基本规范变换迭代(n+k)次产生的规范变换算子Tn+k的行列式表达,并由此给出规范变换后的τ函数Tq(n+k)的形式.     

q-Deformed Gelfand-Dickey系列规范变换的行列式表示

Gelfand-Dickey系列是十分重要的可积系统.本文用规范变换来讨论q-Deformed Gelfand-Dickey系列的解,得到了由两种基本规范变换迭代(n+k)次产生的规范变换算子Tn+k的行列式表达,并由此给出规范变换后的T函数Tq(n+k)的形式.     

一类非线性不连续集值发展型方程的广义单调迭代法

本文利用广义单调迭代法研究了一类非线性不连续集值发展型方程的数值解法,利用序理论给出其迭代格式,得到了迭代解的收敛性结果.在一种较弱的条件下,给出了离散解集收敛性的若干结论.     

参数d迭代逼近的GM(1,1)模型及其在技术创新中的应用

首先论述了参数d迭代逼近求解的GM(1,1)模型基本思路.其次,给出了此模型的参数估计与算法,即:1)估算出初始a_l,根据GM(1,1)模型a,c,d之间的关系,由a_l求得C_l,d_l;2)迭代d_l→d_(l+1),再计算a_(l+1),c_(l+1)及平均相对误差mape_l,mape_(l+1);3)多次迭代d_l→d_(l+1),直至|mape_(l+1)-mape_l|ε时,可得mape最小时的最优参数a,c,d值.然后,从理论与实证方面,证明模型是无偏的,且在参数d迭代过程中,a总能取到有意义的值.最后将模型应用于企业技术创新领域之中.     

求解时谐涡流模型鞍点问题的分块交替分裂隐式迭代算法的改进

分块交替分裂隐式迭代方法是求解具有鞍点结构的复线性代数方程组的一类高效迭代法.本文通过预处理技巧得到原方法的一种加速改进方法,称之为预处理分块交替分裂隐式迭代方法·理论分析给出了新方法的收敛性结果.对于一类时谐涡旋电流模型问题,我们给出了若干满足收敛条件的迭代格式.数值实验验证了新型算法是对原方法的有效改进.     

矩阵多分裂迭代法的收敛条件

本文给出了求解非奇异线性方程组的矩阵多分裂并行迭代法的一些新的收敛结果.当系数矩阵单调和多分裂序列为弱正则分裂时,得到了几个与已有的收敛准则等价的条件,并且证明了异步迭代法在较弱条件下的收敛性.对于同步迭代,给出了与异步迭代不同且较为宽松的收敛条件.     

IFS中一个经典遍历性质的一些推广

该文针对概率迭代函数系统(IFS),给出一些遍历性质,这些结果推广了Elton[2]的结果,一个结果在某种意义上与Fustenberg[4]和Assani [1]关于弱混合系统中的结果类似.     

抛物方程时间周期问题的有限元多格子动力学迭代

本文我们研究线性周期抛物方程的有限元多格子动力学迭代.多格子动力学迭代又称多重网格波形松弛,它是在函数空间中的一种迭代过程.对于由加速技术得到的多格子动力学迭代算子,我们通过计算周期函数的Fourier系数给出了新的谱表达式.从这些有用的表达式出发,我们推导了时间连续和离散格式的迭代收敛条件.数值实验进一步验证了本文的理论结果.     

不连续二阶非线性微分方程的周期边值问题的唯一解

在Banach中,本文在很弱条件下,通过迭代序列得到了不连续二阶非线性微分方程的周期边值问题的唯一解存在性的一个充分条件,而且给出了迭代序列近代解的误差估计.     

不定最小二乘问题的改进的不完全双曲Gram-Schmidt预处理算法

应用改进的不完全双曲Gram-Schmidt(IHMGS)方法预处理不定最小二乘问题的共轭梯度法(CGILS)、正交分解法(ILSQR)与广义的最小剩余法(GMRES)等迭代算法来求解大型稀疏的不定最小二乘问题.数值实验表明,IHMGS预处理方法可有效提高相应算法的迭代速度,且当矩阵的条件数比较大时,效果更加显著.     

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The hybrid censoring scheme is a mixture of type-I and type-II censoring schemes. It is a popular censoring scheme in the literature of life data analysis. Mixed exponential distribution (MED) models is a class of favorable models in reliability statistics. Nevertheless, there is no much discussion to focus on parameters estimation for MED models with hybrid censored samples. We will address this problem in this paper. The EM (Expectation-Maximization) algorithm is employed to derive the closed form of the maximum likelihood estimators (MLEs). Finally, Monte Carlo simulations and a real-world data analysis are conducted to illustrate the proposed method.     

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.     

双边定时截尾样本下广义逆指数分布形状参数的估计

先给出了广义逆指数分布在双边定时截尾样本下形状参数的最大似然估计,并不能得到估计的显式表达式,但证明了参数在(0,+∞)上最大似然估计是唯一存在的.其次提出用EM算法求出形状参数的估计且该估计具有良好的收敛性,还给出了形状参数的EM估计的渐近方差和近似置信区间;最后通过数值模拟,对形状参数的最大似然估计和EM估计的效果进行了比较,说明了用EM算法求形状参数的估计是可行的,并且模拟效果相对比较好.     

MM Algorithms for Variance Components Models

Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum likelihood estimation (MLE) and restricted MLE of variance component models remain numerically challenging. Building on the minorization–maximization (MM) principle, this article presents a novel iterative algorithm for variance components estimation. Our MM algorithm is trivial to implement and competitive on large data problems. The algorithm readily extends to more complicated problems such as linear mixed models, multivariate response models possibly with missing data, maximum a posteriori estimation, and penalized estimation. We establish the global convergence of the MM algorithm to a Karush–Kuhn–Tucker point and demonstrate, both numerically and theoretically, that it converges faster than the classical EM algorithm when the number of variance components is greater than two and all covariance matrices are positive definite. Supplementary materials for this article are available online.     

Acceleration of the EM algorithm using the vector epsilon algorithm

The expectation–maximization (EM) algorithm is a very general and popular iterative computational algorithm to find maximum likelihood estimates from incomplete data and broadly used to statistical analysis with missing data, because of its stability, flexibility and simplicity. However, it is often criticized that the convergence of the EM algorithm is slow. The various algorithms to accelerate the convergence of the EM algorithm have been proposed. The vector ε algorithm of Wynn (Math Comp 16:301–322, 1962) is used to accelerate the convergence of the EM algorithm in Kuroda and Sakakihara (Comput Stat Data Anal 51:1549–1561, 2006). In this paper, we provide the theoretical evaluation of the convergence of the ε-accelerated EM algorithm. The ε-accelerated EM algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the EM algorithm, and thus it keeps the flexibility and simplicity of the EM algorithm.     

基于PCP特征的和弦识别研究与探讨

讨论了音乐识别领域中和弦的四种不同识别方法,给出了基于PCP特征的和弦识别算法.使用PCP作为和弦的特征作为输入送至隐马尔可夫模型中训练,利用Baum-Welch算法估计模型参数,通过Viterbi算法得到正确和弦.通过实验获得了76%的识别率,验证了该算法的可行性.     

基于数据分组与右删失情形下对数正态分布的参数估计

本文研究了对数正态分布数据在分组与删失情形下参数的估计问题. 一是给出未知参数的极大似然估计存在且唯一的充要条件. 二是利用EM算法对参数值进行了估计.     

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.     

Acceleration of the EM algorithm using the Vector Aitken method and its Steffensen form

Based on Vector Aitken (VA) method, we propose an acceleration Expectation-Maximization (EM) algorithm, VA-accelerated EM algorithm, whose convergence speed is faster than that of EM algorithm. The VA-accelerated EM algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the EM algorithm, thus it keeps the flexibility and simplicity of the EM algorithm. Considering Steffensen iterative process, we have also given the Steffensen form of the VA-accelerated EM algorithm. It can be proved that the reform process is quadratic convergence. Numerical analysis illustrate the proposed methods are efficient and faster than EM algorithm.     

A Hybrid High-Order Markov Chain Model for Computer Intrusion Detection

A hybrid model based mostly on a high-order Markov chain and occasionally on a statistical-independence model is proposed for profiling command sequences of a computer user in order to identify a “signature behavior” for that user. Based on the model, an estimation procedure for such a signature behavior driven by maximum likelihood (ML) considerations is devised. The formal ML estimates are numerically intractable, but the ML-optimization problem can be substituted by a linear inverse problem with positivity constraint (LININPOS), for which the EM algorithm can be used as an equation solver to produce an approximate ML-estimate. The intrusion detection system works by comparing a user's command sequence to the user's and others' estimated signature behaviors in real time through statistical hypothesis testing. A form of likelihood-ratio test is used to detect if a given sequence of commands is from the proclaimed user, with the alternative hypothesis being a masquerader user. Applying the model to real-life data collected from AT&T Labs–Research indicates that the new methodology holds some promise for intrusion detection.