线性模型中M估计分布的随机加权方法逼近

在线性模型中,M估计的渐近分布通常都涉及到不易估计的未知误差分布的某些量,如果要估计渐近方差,就需对这些冗余参数进行估计.利用随机加权方法可以避免先对误差分布中的冗余参数进行估计.给出了当自变量是随机变量时,M估计分布的随机加权逼近,证明了M估计分布的随机加权逼近是一致相合的.当取不同的凸函数,样本大小和随机权时,进一步利用蒙特卡洛方法研究估计分布.研究表明随机权取泊松权时,不仅达到同样的效果而且可以减小计算量.     

不完全数据下的分位数估计

本文主要讨论了响应数据缺失时基于无偏估计方程的分位数估计.本文提出了两种非参光滑技术的插补(imputation)方法,一种是整体非参核插补法,另一种是局部多重插补法.我们可以利用这两种方法构造渐近无偏估计方程.通过该缺失数据下的估计方程,我们可以利用常用的估计方法对未知分位数进行统计推断.本文证明了该方法下的分位数估计具有相合性和渐近正态性.     

部分线性混合效应模型中方差分量的稳健估计

部分线性混合效应模型中方差分量是我们感兴趣的参数, 文献中已经给出许多估计方法. 但是其中很多方法都可以归结为广义估计方程方法(GEE), 如: 最大似然估计(MLE), 约束最大似然估计(REMLE)等, 而GEE方法对异常点很敏感. 本文提出一组关于部分线性混合效应模型(PLMM)中均值和方差分量的稳健估计方程, 对均值和方差分量同时进行稳健估计; 并进行了随机模拟考察所提出稳健估计的有效性, 最后通过两个实例, 说明了所提方法的可行性.     

基于泛岭估计对岭估计过度压缩的改进方法

岭估计是解决多元线性回归多重共线性问题的有效方法,是有偏的压缩估计。与普通最小二乘估计相比,岭估计可以降低参数估计的均方误差,但是却增大残差平方和,拟合效果变差。本文提出一种基于泛岭估计对岭估计过度压缩的改进方法,可以改进岭估计的拟合效果,减小岭估计残差平方和的增加幅度。     

基于三次卷积插值的贝叶斯滤波方法研究

针对一类二维空间系统的状态估计模型,提出了一种用三次卷积插值方法递推估计的非线性滤波算法.仿真实例采用一个常用的非线性模型,并与粒子滤波算法进行对比分析,仿真结果表明三次卷积插值方法提高滤波估计精度,从而验证其估计一类状态估计模型解析解的可行性,其插值算法还可以推广到多维空间系统.     

纵向数据部分线性测量误差模型的二次推断函数估计

基于纵向数据部分线性测量误差模型, 研究了模型中兴趣参数部分回归系数的估计问题. 首先采用B样条方法逼近模型中的非参数函数, 然后提出修正的二次推断函数(QIF)方法对模型中参数部分的回归系数进行估计, 所提方法可以提高估计的效率. 在一定的正则条件下, 证明了所得到的估计量具有相合性和渐近正态性. 最后, 通过模拟研究和实例分析验证了所提出估计方法的有限大样本性质.     

多元失效时间删失数据边际风险模型加权估计的渐近正态性

对于多元失效时间数据,可以根据工作独立的假定来估计边际风险模型中的未知参数,但工作独立方法通常会失去估计的效率.为了充分利用不同失效类型之间的潜在相关性,提高估计的效率,可以通过加权的方法给出参数的加权部分似然估计.然而由于多元失效数据是高维数的数据,选择最优权是困难的.因此,Fan,Zhou,Cai和Chen曾基于参数估计向量中每个元的方差提出了一些次优加权方法,然后从参数向量所有分量估计的角度出发,构造了未知参数的复合加权部分似然估计,但他们没有给出这些复合加权估计的渐近性质.本文将对复合加权部分似然估计进一步的研究,推导了这个估计的渐近正态性,并给出了该估计的协方差阵以及协方差估计.同时,将该方法应用于艾滋病临床试验的实际数据,给出了有意义的解释和说明.最后进行了相关估计的一些数值模拟计算.     

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.     

一类基于秩次的稳健线性回归估计与诊断方法

线性回归模型的误差项不服从正态分布或存在多个离群点时,可以将残差秩次的某些函数作为权重引入估计模型来减少离群点的不良影响。本文从参数估计、稳健性质、回归诊断等方面对基于残差秩次的一类稳健回归方法进行介绍.通过模拟研究和实例分析表明,R和GR估计是一种估计效率较高的稳健回归方法,其中GR估计可同时避免X与Y空间离群点,而高失效点HBR估计可通过控制某个参数在稳健性与估计效率之间进行折衷.     

基于分层贝叶斯广义线性模型的小域估计方法研究

当前,构建恰当的小域估计方法是解决我国政府抽样调查中多层次推断问题的关键所在。由于小域的小样本特性,基于频率统计学的小域估计方法推断效果并不理想,而传统基于贝叶斯统计视角的小域估计方法在非连续型变量估计时适应性不强。本文在系统介绍传统贝叶斯小域估计方法的基站上,为了解决离散变量的估计推断问题,将广义线性模型引入到分层贝叶斯方法中,构建了基本的理论机制和分类数据的估计模型。基于此模型,运用全国流动人口动态监测调查2014年广东省内的样本数据进行实例测算,估计出广东省各地级市的流动人口学历分布情况,并将分层贝叶斯广义线性模型的估计结果与传统估计方法进行了对比分析。结果显示,分层贝叶斯广义线性模型在样本量充足的情况下能够准确地估计出目标小域的总体参数,在样本量不足的小域中依然能够给出稳健的估计结果。文章所构建的估计模型不仅可以充分利用先验信息和辅助信息,还适用于对复杂数据进行估计推断,能够为我国政府抽样调查的小域估计实践提供有价值的理论参考。     

Global and mid-range function approximation for engineering optimization

Global and mid-range approximation concepts are used in engineering optimisation in those cases were the commonly used local approximations are not available or applicable. In this paper the response surface method is discussed as a method to build both global and mid-range approximations of the objective and constraint functions. In this method analysis results in multiple design points are fitted on a chosen approximation model function by means of regression techniques. Especially global approximations rely heavily on appropriate choices of the model functions. This builds a serious bottleneck in applying the method. In mid-range approximations the model selection is much less critical. The response surface method is illustrated at two relatively simple design problems. For building global approximations a new method was developed by Sacks and co-workers, especially regarding the nature of computer experiments. Here, the analysis results in the design sites are exactly predicted, and model selection is more flexible compared to the response surface method. The method will be applied to an analytical test function and a simple design problem. Finally the methods are discussed and compared.     

Better Optimization of Nonlinear Uncertain Systems (BONUS) for Vehicle Structural Design

With the continuous improvement of computational performance, vehicle structural design has been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization approaches generate deterministic optimal designs without considering variability effects in modeling, simulation, and/or manufacturing. One of the main reasons for this omission is due to the fact that the computing time of a single crash analysis for vehicle structural design still requires significant computing time using a state-of-the-art computer. This calls for the development and implementation of an efficient optimization under uncertainty method. In this paper, a new integrated stochastic optimization method, which combines the advantages of metamodeling techniques and Better Optimization of Nonlinear Uncertain Systems (BONUS), is developed for vehicle side impact design. Nonlinear metamodels are built by using a stepwise regression method to replace the expensive computational model and BONUS is employed to obtain optimal designs under uncertainty. A benchmark problem for vehicle safety design is used to demonstrate the method. The main goal of this case study is to maintain or enhance the vehicle side impact test performance while minimizing the vehicle weight under various uncertainties.     

Detection,isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The goal in many fault detection and isolation schemes is to increase the isolation and identification speed. This paper, presents a new approach of a nonlinear model based adaptive observer method, for detection, isolation and identification of actuator and sensor faults. Firstly, we will design a new method for the actuator fault problem where, after the fault detection and before the fault isolation, we will try to estimate the output of the instrument. The method is based on the formation of nonlinear observer banks where each bank isolates each actuator fault. Secondly, for the sensor problem we will reformulate the system by introducing a new state variable, so that an augmented system can be constructed to treat sensor faults as actuator faults. A method based on the design of an adaptive observers’ bank will be used for the fault treatment. These approaches use the system model and the outputs of the adaptive observers to generate residues. Residuals are defined in such way to isolate the faulty instrument after detecting the fault occurrence. The advantages of these methods are that we can treat not only single actuator and sensor faults but also multiple faults, more over the isolation time has been decreased. In this study, we consider that only abrupt faults in the system can occur. The validity of the methods will be tested firstly in simulation by using a nonlinear model of waste water treatment process with and without measurement noise and secondly with the same nonlinear model but by using this time real data.     

A new hybrid uncertainty optimization method for structures using orthogonal series expansion

This paper proposes a new hybrid uncertain design optimization method for structures which contain both random and interval variables simultaneously. The optimization model is formulated with the feasible robustness and the reliability of the worst scenario. The hybrid uncertainty is quantified by using the orthogonal series expansion method that integrates the Polynomial Chaos (PC) expansion method and the Chebyshev interval method within a uniform framework. The design sensitivity of objective and constraints will be developed to greatly facilitate the use of gradient-based optimization algorithms. The numerical results show that this method will be more possible to seek the feasible solution.     

一种确定项目工作团队合理规模的新思路

本主要研究项目工作团队合理规模确定问题，基于委托—代理理论，建立了项目工作团队管理激励与约束机制模型，探讨了项目工作团队规模的设计问题，给出了一种新的项目工作团队合理规模设置的有效方法。从激励与约束角度研究项目工作团队合理规模的确定具有创新性，丰富了项目工作团队规模设计的理论与方法。     

Iterative identification and control design using finite-signal-to-noise models

In this paper, a model is said to be validated for control design if using the model-based controller, the closed loop performance of the real plant satisfies a specified performance bound. To improve the model for control design, only closed loop response data is available to deduce a new model of the plant. Hence the procedure described herein involves three steps in each iteration: (i) closed loop identification; (ii) plant model extraction from the closed loop model; (iii) controller design. Thus our criteria for model validation involve both the control design procedure by which the closed loop system performance is evaluated, and the identification procedure by which a new model of the plant is deduced from the closed loop response data. This paper proposes new methods for both parts, and also proposes an iterative algorithm to connect the two parts. To facilitate both the identification and control tasks, the new finite-signal-to-noise (FSN) model of linear systems is utilized. The FSN model allows errors in variables whose noise covariances are proportional to signal covariances. Allowing the signal to noise ratios to be bounded but uncertain, a control theory to guarantee a variance upper bound is developed for the discrete version of this new FSN model. The identification of the closed loop system is accomplished by a new type of q-Markov Cover, adjusted to accommodate the assumed FSN structure of the model. The model of the plant is extracted from the closed loop identification model. This model is then used for control design and the process is repeated until the closed loop performance validates the model. If the iterations produce no such a controller, we say that this specific procedure cannot produce a model valid for control design and the level of the required performance must be reduced.     

垂直裂缝井试井分析模型和方法

根据压裂井的流动机制,综合考虑井筒储存、裂缝壁面污染和各种边界条件,通过组合线性流模型与有效井径模型,建立了垂直裂缝井试井分析的新模型,提出了确定垂直裂缝井有效井径的方法,给出了有效井径随裂缝长度、裂缝导流能力和裂缝表皮系数的变化关系．该模型形式简明,曲线完整,计算速度快,可满足实时计算和快速响应的试井解释要求．以实测的压力或产量为拟合目标函数,建立了识别地层和水力裂缝参数的最优化模型,提出了综合应用逐步线性最小二乘法和约束变尺度法的垂直裂缝井试井分析自动拟合方法,并通过算例说明了该方法的准确性和可靠性．应用情况表明,该技术可科学合理地分析评估压裂施工质量,指导并改进压裂设计,提高压裂设计水平和施工效果．     

An optimization-based approach for the design of Bayesian networks

Bayesian networks model conditional dependencies among the domain variables, and provide a way to deduce their interrelationships as well as a method for the classification of new instances. One of the most challenging problems in using Bayesian networks, in the absence of a domain expert who can dictate the model, is inducing the structure of the network from a large, multivariate data set. We propose a new methodology for the design of the structure of a Bayesian network based on concepts of graph theory and nonlinear integer optimization techniques.     

On a New Diffeomorphic Multi-Modality Image Registration Model and Its Convergent Gauss-Newton Solver

In this work, we propose a new variational model for multi-modal image registration and present an efficient numerical implementation. The model minimizes a new functional based on using reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. A key feature of the model is its ability of guaranteeing a diffeomorphic transformation which is achieved by a control term motivated by the quasi-conformal map and Beltrami coefficient. The existence of the solution of this model is established. To solve the model numerically, we design a Gauss-Newton method to solve the resulting discrete optimization problem and prove its convergence; a multilevel technique is employed to speed up the initialization and avoid likely local minima of the underlying functional. Finally, numerical experiments demonstrate that this new model can deliver good performances for multi-modal image registration and simultaneously generate an accurate diffeomorphic transformation.     

一种新的交通网络设计优化算法

交通网络设计问题是研究如何用定量的方法在已有交通网络上添加或扩容某些路段的问题.文章在回顾交通网络设计问题文献的基础上,提出了基于图论网络优化思想的解决该类问题的一种新思路,给出了启发式算法,并进行了算法复杂性分析,最后通过算例验证了其有效性.