半参数广义线性混合效应模型的估计及其渐近性质

半参数广义线性混合效应模型在心理学、生物育种、医学等领域有广泛的应用. Zhang(1998)用最大惩罚似然函数的方法(MPLE)对模型的参数和非参数部分进行了估计, 而Zhang (1998) MPLE方法只适用于正态数据模型. 对于泊松等常用的模型, 常的方法是将随机效应看作缺失数据, 再引入EM算法. 本文基于McCulloch 1997)提出的MCNR算法, 此算法推广到半参数广义线性混合效应模型中并得到相应的估计算法. 于非参数部分, 本文采用P样条拟合并利用GCV方法选取光滑参数, 时证明了所得估计的相合性和渐近正态性. 最后, 过模拟和实例与其它算法作比较验证本文估计方法的有效性.     

动物生长模型的优化拟合方法

动物生长模型的优化拟合过程应分为模型参数的优化估计与拟合结果的统计检验两个步骤。现有的曲线拟合方法中，Ｍａｒｑｕａｒｄｔ法是比较公认的最优参数估计法，本文利用Ｍａｒｑｕａｒｄｔ法估计了几种常用动物生长模型的参数，为保证Ｍａｒｑｕａｒｄｔ法优化估计参数的成功，对参数初值的选取及阻尼因子的确定提出了参考意见；在拟合结果的统计检验方面，针对目前普遍只采用拟合度的大小来说明拟合好坏的情况，又进一步探索了对拟合结果进行统计检验的方法．     

纵向数据半参数回归模型的估计方法

本文考虑纵向数据半参数回归模型,通过考虑纵向数据的协方差结构,基于Profile最小二乘法和局部线性拟合的方法建立了模型中参数分量、回归函数和误差方差的估计量,来提高估计的有效性,在适当条件下给出了这些估计量的相合性.并通过模拟研究将该方法与最小二乘局部线性拟合估计方法进行了比较,表明了Profile最小二乘局部线性拟合方法在有限样本情况下具有良好的性质.     

Morgan-Mercer-Flodin模型和Weibull模型的拟合

Morgan-Mercer-Flodin模型和 Weibull模型是两个著名的四参数 S形生长模型 .在一定的正则变换下 ,它们的隐函数方程及未线性化参数与拐点特征之间的联系都非常相似 .从而可用完全相同的方法对它们的未线性化参数初始值进行搜索 ,以拟合隐函数曲线的 GNL法对它们进行最小二乘拟合 .还用实例对这种算法进行了验证 .     

基于马尔科夫状态转移模型的天气衍生品定价

引入马尔科夫状态转移(MRS)模型拟合长沙市每日平均气温变化,利用最大期望算法估计马尔科夫状态转移模型参数,通过误差分析得到了最佳MRS模型.基于最佳的MRS模型,采用无套利定价原理定价气温衍生品,并利用蒙特卡罗方法得到了取暖指数(HDD)欧式看涨期权的数值解.实证结果表明,五状态的MRS模型对长沙市每日平均气温变化的拟合效果明显优于其他的MRS模型,它使得气温衍生品定价结果相比以前的方法更为精确.     

基于信息再利用的灰色系统GM(1.1)模型建模方法及应用

目的:寻找新的灰色系统GM(1.1)模型建模方法,建立拟合精度与预测精度较高的GM(1.1)模型.方法:在邓聚龙教授建模方法的基础上,用基于信息再利用的方法,建立新的灰色系统GM(1.1)模型.结果:用基于信息再利用的灰色系统GM(1.1)模型建模方法建立的GM(1.1)模型,其拟合精度与预测精度不但优于传统方法建立的GM(1.1)模型,而且优于其他改进方法建立的GM(1.1)模型.结论:基于信息再利用的灰色系统GM(1.1)模型建模方法不但建模过程简单适用,而且其建立的GM(1.1)模型拟合精度与预测精度优于其他改进方法建立的GM(1.1)模型,因而具有广泛的应用价值.     

总体最小二乘准则下非线性EV模型的参数拟合方法

在非线性回归模型参数拟合问题中,当数据中的每个变量都存在不可忽略的误差时,在普通的最小二乘准则下拟合出的参数不是最优的.按照总体最小二乘准则,以观测点到拟合曲线或拟合曲面垂直距离平方和为目标函数,然后用最优化方法搜索出使目标函数值取最小值的参数和数据点估计,从而给出求最优模型参数的算法,最后,通过计算机仿真和与文献比较,验证了提出方法的正确性.     

考虑双拉耦合的复合材料编织物非正交本构模型

基于连续介质力学理论,将复合材料编织物的双轴拉伸效应引入到前期提出的非正交本构模型中,提出了一种考虑双拉耦合的复合材料编织物非正交本构模型.给出了模型参数的确定方法,并通过拟合单轴拉伸、不等比双轴拉伸和偏轴拉伸实验数据,得到了本构模型参数.利用该模型对双轴拉伸和双球冲压实验进行了有限元模拟,并将模拟结果和实验结果进行对比,验证了所提出本构模型的可靠性,该模型能更好地表征复合材料编织物在成形过程中由于大变形所引起的非线性各向异性力学行为.这一本构模型具有结果精确、参数容易确定的优点,为编织复合材料成形的数值模拟和成形工艺优化奠定了理论基础.     

Richards模型参数估计及其模型应用

在非线性模型中,Richards模型是一个含有四参数的增长曲线模型,该模型对数据的拟合有较强的适应性,应用较为广泛.但其参数的估计较为复杂,给出简便易行的三种方法,实例应用表明拟合效果很好.     

删失数据下的半参数变系数部分线性回归模型

为了分析删失数据,该文考虑变系数部分线性模型,此模型允许协变量对响应变量存在非线性影响.响应变量与协变量之间关系的统计模型通过线性结构来拟合是非常重要而且有益.对于删失数据,常用的统计方法不能直接应用于此模型.该文首先提出一类数据变换用以建立无偏条件期望.然后利用profile最小二乘方法,给出了模型中参数分量和非参数分量的profile最小二乘估计,并建立了这些估计的渐近正态性.最后通过数值例子来说明该文所提出的方法的有效性.     

The reliability analysis of probabilistic and interval hybrid structural system

The aim of this paper is to evaluate the reliability of probabilistic and interval hybrid structural system. The hybrid structural system includes two kinds of uncertain parameters—probabilistic parameters and interval parameters. Based on the interval reliability model and probabilistic operation, a new probabilistic and interval hybrid reliability model is proposed. Firstly, we use the interval reliability model to analyze the performance function, and then sum up reliability of all regions divided by the failure plane. Based on the presented optimal criterion enumerating the main failure modes of hybrid structural system and the relationship of failure modes, the reliability of structure system can be obtained. By means of the numerical examples, the hybrid reliability model and the traditional probabilistic reliability model are critically contrasted. The results indicate the presented reliability model is more suitable for analysis and design of these structural systems and it can ensure the security of system well, and it only needs less uncertain information.     

AR模型辨识的稀疏结构迭代算法

模型估计是机器学习领域一个重要的研究内容,动态数据的模型估计是系统辨识和系统控制的基础.针对AR时间序列模型辨识问题,证明了在给定阶数下AR模型参数的最小二乘估计本质上也是一种矩估计.根据结构风险最小化原理,通过对模型拟合度和模型复杂度的折衷,提出了基于稀疏结构迭代的AR序列模型估计算法,并讨论了基于广义岭估计的最优正则化参数选取规则.数值结果表明,方法能以节省参数的方式有效地实现AR模型的辨识,比矩估计法结果有明显改善.     

线性定常系统特征模型的特征参量辨识

说明线性定常系统特征模型的特征参量是一组由高阶线性定常系统的相关信息压缩而成,于是不能简单的作为与状态无关的慢时变参数来处理. 基于特征建模思想,建立了线性定常系统特征模型的特征参量与子空间方法之间的联系,给出了一种该特征模型的特征参量 的合成辨识算法.同时证明了在用于子空间辨识的样本量充分大和用于状态估计的时间充分长的情况下, 特征参量的估计值与真值之间的误差达到充分小. 最后,对于一个六阶的单输入单输出线性定常系统的仿真例子,对投影的带遗忘因子最小二乘算法和合成辨识算法进行了比较,验证了合成辨识算法的有效性.     

Non-linear model parameter estimation–estimating a feasible parameter set with respect to model use

This article deals with non-linear model parameter estimation from experimental data. As for non-linear models a rigorous identifiability analysis is difficult to perform, parameter estimation is performed in such a way that uncertainty in the estimated parameter values is represented by the range of model use results when the model is used for a certain purpose. Using this approach, the article presents a simulation study where the objective is to discover whether the estimation of model parameters can be improved, so that a small enough range of model use results is obtained. The results of the study indicate that from plant measurements available for the estimation of model parameters, it is possible to extract data that are important for the estimation of model parameters relative to a certain model use. If these data are improved by a proper measurement campaign (e.g. proper choice of measured variables, better accuracy, higher measurement frequency) it is to be expected that a valid model for a certain model use will be obtained. The simulation study is performed for an activated sludge model from wastewater treatment, while the estimation of model parameters is done by Monte Carlo simulation.     

Sensitivity analysis of the combined travel demand model with applications

The conventional sequential four-step procedure of travel demand forecasting has been widely adopted by practitioners. However, it suffers from inconsistent consideration of travel times and congestion effects in various steps of the procedure. A combined travel demand model overcomes the problems associated with the sequential four-step procedure by integrating travel-destination-mode-route choice together. In this paper, a standard sensitivity analysis for non-linear programming is employed for conducting the sensitivity analysis of the combined travel demand model. Explicit expressions of the derivatives of model variables with respect to perturbations of input variables and parameters of the combined travel demand model are developed. These derivatives could be used to assess changes in solution variables and various system performance measures when the network characteristics are changed slightly. To gain insight into the usefulness of the sensitivity expressions, five applications, such as identification of critical parameters, paradox analysis, access control, destination choice, and error and uncertainty analysis, are presented with numerical results.     

Tuberculosis model with relapse via fractional conformable derivative with power law

The present study explores the tuberculosis dynamics with relapse with a nonlocal conformable derivative in the Caputo sense. The real data of tuberculosis cases since 2002 to 2017 are used to the set the parameters. The numerical results with the realistic parameters are chosen and present the graphical results. Further, we assign different values to the fractional parameters α and β and discuss its effect on the system variables. The use of these two fractional operators on the model simultaneously with realistic data gives reliable results.     

An integrated simulation-optimization framework for the operational planning of seaport container terminals

In this article the operational planning of seaport container terminals is considered by defining a suitable integrated framework in which simulation and optimization interact. The proposed tool is a simulation environment (implemented by using the Arena software) representing the dynamics of a container terminal. When the system faces some particular conditions (critical events), an optimization procedure integrated in the simulation tool is called. This means that the simulation is paused, an optimization problem is solved and the relative solution is an input for the simulation environment where some system parameters are modified (generally, the handling rates of some resources are changed). For this reason, in the present article we consider two modelling and planning levels about container terminals. The simulation framework, based on an appropriate discrete-event model, represents the dynamic behaviour of the terminal, thus it needs to be quite detailed and it is used as an operational planning tool. On the other hand, the optimization approach is devised in order to define some system parameters such as the resource handling rates; in this sense, it can be considered as a tool for tactical planning. The optimization procedure is based on an aggregate representation of the terminal where the dynamics is modelled by means of discrete-time equations.     

基于粒子群的模糊神经网络的短时交通流量组合预测

为了进一步提高短时交通流量预测的精度,提出了一种粒子群算法的模糊神经网络组合预测模型,模糊神经网络融合了神经网络的学习机制和模糊系统的语言推理能力等优点,弥补各自不足,将自回归求和滑动平均(ARIMA)和灰色Verhulst模型进行初步预测,并将两种初步预测的结果作为模糊神经网络的输入,构建基于改进模神经网络的组合预测模型,在此基础上进行训练和预测,其中模糊神经网络的相关参数由改进粒子群来优化,利用本方法来对南京市汉中路短时交通流量进行预测,结论表明:方法充分发挥了单一模型的优势,比单一的预测模型更加精确,是短时交通流量预测的一个有效方法。     

Studies on the Interaction Mechanism between the mRNA Vaccine against SARS-CoV-2 and the Immune System

Vaccines are an effective tool in the fight against infectious diseases. However, mathematical models of SARS-CoV-2 focus on the macroscopic situation, while articles on vaccines focus on effectiveness and safety. We develop four mathematical models to investigate the immune system and the microdynamics of antigens and viruses in individuals injected with mRNA vaccines. We first theoretically analyze the optimal model, calculate all equilibria, and prove that the disease-free equilibrium is globally asymptotically stable while the others are unstable. This suggests that after a certain period after vaccination, the infected cells and antigens will no longer exist in vivo and will be eliminated by the immune system over time or will die naturally. This theoretically proves the safety of the mRNA vaccines. Then, we use the differential algebra to analyze the structural identifiability of the models. We find that two of them are globally identifiable while the other two are unidentifiable, but once a certain parameter is fixed, then they are identifiable as well. To select the optimal model among four models, we use the Affine Invariant Ensemble Markov Chain Monte Carlo algorithm for data fitting and parameter estimation. We find that the roles of memory cells in killing infected cells and promoting immune cells and neutralizing antibodies in the process of mRNA vaccination are not significant and can be ignored in the modeling. On the other hand, the innate immunity of the human body plays an important role in this process. In addition, we also analyze the practical identifiability of the parameters of the optimal model. The results show that even if the structure of the system is globally identifiable, it does not ensure that all the parameters are practically identifiable. After random sampling and simulating the four unidentifiable parameters, we find that only two variables, infected cells II and antibodies, are sensitive to these unidentifiable parameters, but the results are still within acceptable ranges. This suggests that our fitting results are generally reliable. Finally, we simulate multiple booster injections and find that booster injections are indeed effective in maintaining antibody levels in vivo, which could otherwise gradually die off over time. Therefore, booster injections are beneficial to help the human body increase and maintain immunity.     

人体体循环输入阻抗的T-Y型管模型*

本文提出了模拟人体体循环输入阻抗的T－Y型管模型,它是O'Rourke[1]提出的,柳兆荣等[2]发展的非对称T型管模型的改进和推广.我们在非对称T型管基础上加添了代表人体两条腿的倒Y字型分叉管,从而形成了T－Y型模型,同时将模型中所有分支管都处理成具有纵向约束的粘弹性管,上肢终端小动脉床采用弹性腔模型,下肢终端小动脉床则采用传统的纯阻力形式.选取了适当参数值后,计算了正常生理情形下和高血压病理情形下的人体体循环的输入阻抗.结果显示理论模拟值和实测结果无论是生理情形或是病理情形都有良好的符合程度.T－Y型管模型比非对称T型管模型更接近人体体循环的生理结构.同时又比过份复杂的多重分叉管模型简单明了得多,因此在考察各种参数对升主动脉输入阴抗的影响.研究心室和血管的耦合机制中将是一个有实用价值的模型.