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.     

二元广义Weibull模型(英文)

本文研究了一个二元广义Weibull分布模型,其边缘分布分别是一元广义Weibull分布.利用EM算法,得到了未知参数的极大似然估计和观测Fisher信息矩阵.     

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.     

删失截断情形下Weibull分布多变点模型的参数估计

通过添加缺失的寿命变量数据,得到了删失截断情形下Weibull分布多变点模型的完全数据似然函数,研究了变点位置参数和形状参数以及尺度参数的满条件分布.利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法得到了参数的Gibbs样本,把Gibbs样本的均值作为各参数的Bayes估计.详细介绍了MCMC方法的实施步骤.随机模拟试验的结果表明各参数Bayes估计的精度都较高.     

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.     

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.     

截断删失情形下泊松分布参数多变点的Bayes估计

通过添加数据得到截断删失情形下泊松分布的完全数据似然函数,研究变点位置和其它参数的满条件分布.利用Gibbs抽样与Metropolis-Hastings算法相结合的MCMC方法对参数进行估计,详细介绍MCMC方法的实施步骤.随机模拟试验的结果表明参数Bayes估计的精度较高.     

带有不完全信息随机截尾试验下混合泊松分布参数的点估计

首先,通过添加数据,得到了带有不完全信息随机截尾试验下混合泊松分布的完全数据似然函数,然后分别利用EM算法和MCMC方法,对参数进行了估计,最后进行了随机模拟试验.结果表明参数点估计的精度比较高,     

一个新的具有单调降失效率的寿命分布

本文由Pareto分布和Logarithmic分布"混合"生成两参数具有单调降失效率的新型寿命分布,研究了该分布的矩、熵、失效率函数、平均剩余寿命和参数的极大似然估计,应用EM算法求参数的极大似然估计,进行了数值模拟.     

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。     

On Convergence of Convex Minorant Algorithms for Distribution Estimation with Interval-Censored Data

Abstract

Local convergence results of the convex minorant (CM) algorithm to obtain the nonparametric maximum-likelihood estimator of a distribution under interval-censored observations are given. We also provide a variation of the CM algorithm, which yields global convergence. The algorithm is illustrated with data on AIDS survival time in 92 members of the U.S. Air Force.     

On the Orderings and Groupings of Conditional Maximizations within ECM-Type Algorithms

Abstract

The ECM and ECME algorithms are generalizations of the EM algorithm in which the maximization (M) step is replaced by several conditional maximization (CM) steps. The order that the CM-steps are performed is trivial to change and generally affects how fast the algorithm converges. Moreover, the same order of CM-steps need not be used at each iteration and in some applications it is feasible to group two or more CM-steps into one larger CM-step. These issues also arise when implementing the Gibbs sampler, and in this article we study them in the context of fitting log-linear and random-effects models with ECM-type algorithms. We find that some standard theoretical measures of the rate of convergence can be of little use in comparing the computational time required, and that common strategies such as using a random ordering may not provide the desired effects. We also develop two algorithms for fitting random-effects models to illustrate that with careful selection of CM-steps, ECM-type algorithms can be substantially faster than the standard EM algorithm.     

A data parallel augmenting path algorithm for the dense linear many-to-one assignment problem

The purpose of this study is to describe a data parallel primal-dual augmenting path algorithm for the dense linear many-to-one assignment problem also known as semi-assignment. This problem could for instance be described as assigning n persons to m(n) job groups.The algorithm is tailored specifically for massive SIMD parallelism and employs, in this context, a new efficient breadth-first-search augmenting path technique which is found to be faster than the shortest augmenting path search normally used in sequential algorithms for this problem. We show that the best known sequential computational complexity of O(mn ² ) for dense problems, is reduced to the parallel complexity of O(mn), on a machine with n processors supporting reductions in O(1) time. The algorithm is easy to implement efficiently on commercially available massively parallel computers. A range of numerical experiments are performed on a Connection Machine CM200 and a MasPar MP-2. The tests show the good performance of the proposed algorithm.     

Construction of CM Picard curves

In this article we generalize the CM method for elliptic and hyperelliptic curves to Picard curves. We describe the algorithm in detail and discuss the results of our implementation.

     

Intrinsically parallel solution of systems of linear partial differential equations

We give here an intrinsically parallel algorithm, which solves systems of linear partial differential equations. The mathematical foundations of the algorithm rely upon a particular representation of polynomials on a structure called “hypercube”, introduced by Beauzamy-Frot-Millour, and use Bombieri's scalar product. This scalar product also permits a detailed study of the stability of the algorithm. Boundary conditions and compatibility conditions are handled by the algorithm in an intrinsically parallel manner. This algorithm has been implemented on a Connection Machine CM5, at the “Etablissement Technique Central de l'Armement” (Arcueil, France). We give here several numerical examples.     

A genetic algorithm for cellular manufacturing design and layout

Cellular manufacturing (CM) is an approach that can be used to enhance both flexibility and efficiency in today’s small-to-medium lot production environment. The design of a CM system (CMS) often involves three major decisions: cell formation, group layout, and group schedule. Ideally, these decisions should be addressed simultaneously in order to obtain the best results. However, due to the complexity and NP-complete nature of each decision and the limitations of traditional approaches, most researchers have only addressed these decisions sequentially or independently. In this study, a hierarchical genetic algorithm is developed to simultaneously form manufacturing cells and determine the group layout of a CMS. The intrinsic features of our proposed algorithm include a hierarchical chromosome structure to encode two important cell design decisions, a new selection scheme to dynamically consider two correlated fitness functions, and a group mutation operator to increase the probability of mutation. From the computational analyses, these proposed structure and operators are found to be effective in improving solution quality as well as accelerating convergence.     

基于ELM算法的羊肉水分含量的快速无损检测

在近红外光谱900-1700nm的波长范围内采集南疆羊肉的光谱数据,来研究水分含量的快速无损检测.为减弱非目标因素对光谱的影响,采用SNV和去趋势法对光谱数据进行预处理.为降低建模的复杂度,去除共线性的影响,采用连续投影算法和相关系数法相结合选取8个特征波长变量,最后使用PLS和ELM算法分别进行建模.实验表明,与采用全光谱波段建模相比,采用特征波长变量建模,PLS和ELM算法的运行时间都大大缩短,并且在运行时间和预测精度上,ELM算法均优于PLS算法.ELM算法采用8个特征波段变量建模,预测精度达到0.9768,均方误差为4.4291e-04,相关系数为0.7603,运行时间可控制在1e-04s之下,这可为研发羊肉水分含量的便携式检测装置提供理论参考.     

Affine-scaling for linear programs with free variables

The affine-scaling modification of Karmarkar's algorithm is extended to solve problems with free variables. This extended primal algorithm is used to prove two important results. First the geometrically elegant feasibility algorithm proposed by Chandru and Kochar is the same algorithm as the one obtained by appending a single column of residuals to the constraint matrix. Second the dual algorithm as first described by Adler et al., is the same as the extended primal algorithm applied to the dual.     

求解无容量设施选址问题的拉格朗日蝙蝠算法

无容量设施选址问题(Uncapacitated Facility Location Problem,UFLP)是一类经典的组合优化问题,被证明是一种NP-hard问题,易于描述却难于求解.首先根据UFLP的数学模型及其具体特征,重新设计了蝙蝠算法的操作算子,给出了求解UFLP的蝙蝠算法.其次构建出三种可行化方法,并将其与求解UFLP的蝙蝠算法和拉格朗日松弛算法相结合,设计了求解该问题的拉格朗日蝙蝠算法.最后通过仿真实例和与其他算法进行比较的方式,验证了该混合算法用来求解UFLP的可行性,是解决离散型问题的一种有效方式.     

基于蝙蝠退火算法的无等待流水线调度问题研究

无等待流水线调度问题(no-wait flow shop scheduling problem,NWFSP)是一类比较重要的复杂生产调度问题,并已经被证明是典型的NP问题.蝙蝠算法(Bat algorithm,BA)是一种较新颖的群体智能算法.本文针对蝙蝠算法在求解无等待流水线调度问题上的不足,提出一种蝙蝠退火算法,它通过采用ROV的编码方式以实现离散问题的连续编码,同时为了避免算法早熟现象引入了模拟退火算法.算法采用基于NEH的局部搜索规则,在很大程度上提高了算法的性能.利用标准Car问题和Rec问题算例进行仿真实验,结果表明了改进算法的可行性和有效性.