一种参数估计方法及其在可靠性增长分析中的应用

在可靠性增长分析中,研究Gompertz模型,Duane模型和AMSAA模型的参数点估计方法的文献有很多。本文应用微分回归方法,获得了上述模型参数的又一种点估计,其优点在于:点估计的表达式能显式给出且计算简便。     

经常购买模型及其参数的极大似然估计

在分析巴斯模型和重复购买的产品生命周期模型等的基础上，提出经常购买模型，详细讨论该模型的参数的极大似然估计法，并采用实例检验模型及参数估计方法，分析模型的参数估计结果，在此基础上使用模型做产品销量预测，表明模型的参数估计方法及预测方法的有效性。     

Frequentist model averaging estimation: a review

In applications, the traditional estimation procedure generally begins with model selection. Once a specific model is selected, subsequent estimation is conducted under the selected model without consideration of the uncertainty from the selection process. This often leads to the underreporting of variability and too optimistic confidence sets. Model averaging estimation is an alternative to this procedure, which incorporates model uncertainty into the estimation process. In recent years, there has been a rising interest in model averaging from the frequentist perspective, and some important progresses have been made. In this paper, the theory and methods on frequentist model averaging estimation are surveyed. Some future research topics are also discussed.     

基于GA和BP融合算法的装备费用估算方法

简要介绍了GA和BP算法以及装备费用估算分析的传统方法 ,分析了GA、BP算法以及费用估算三者间相互结合的必要性。把GA和BP算法相结合并应用到装备费用估算分析领域 ,建立了基于GA和BP融合算法的防空导弹研制费用评估模型 ,并对典型的防空导弹型号研制费用进行了估算。结果表明 ,该模型具有计算精度高、适用范围广、运算速度快的特点 ,为装备费用估算提供了有效依据     

Box—Cox变换模型模拟矩估计方法研究

基于对Ｂｏｘ－Ｃｏｘ变换模型参数估计方法的研究，分析了极大似然方法之不足，提出了相应参数估计方法——基于残差的广义模拟矩估计方法，并研制了相应的算法．该方法同时解决了Ｂｏｘ－Ｃｏｘ变换模型出现序列相关与异方差时的参数估计问题，力求反映真实模型．算例则验证了方法的有效性     

基于BP神经网络的路径行程时间实时预测模型

行程时间预测是交通流诱导系统研究的一项重要内容．在分析各种行程时间预测方法的基础上，本文建立了基于ＢＰ神经网络的行程时间实时预测模型，编制了行程时间预测软件系统．利用长春市的交通实测数据对行程时间进行了预测     

基于多保真度代理模型的装备系统参数估计方法

系统参数估计是武器装备系统设计研制中的关键一环。针对复杂系统设计中逐渐突显的系统复杂性激增、需求动态性变化、响应的快速性、缺乏高精度数据等问题,提出一种基于多保真度代理模型的单装备系统参数估计方法。首先,该方法基于传统Kriging模型对低保真度数据建立代理模型。然后,将该模型作为全局趋势模型,插入高保真度数据建立多保真度代理模型,得到同时满足高精度和高效率双重要求的参数估计方法。最后,以飞机机翼系统设计中的最大升力系数参数估计问题为例,验证了所提方法在装备设计研制中的可行性和有效性,作为设计人员的决策支撑。     

空间谱估计算法结构及仿真分析

在对高分辨空间谱估计算法研究的基础上,提出了空间谱估计算法的基本结构,这种结构包含两个组成部分预处理算法和波达方向估计(DirectionofArrival,DOA)算法.同时对多种预处理算法和DOA估计算法进行了分类说明.最后通过大量的计算机仿真实验比较了各种算法的性能.     

Approach for wideband direction-of-arrival estimation in the presence of array model errors

The presence of array imperfection and mutual coupling in sensor arrays poses several challenges for development of effective algorithms for the direction-of-arrival (DOA) estimation problem in array processing. A correlation domain wideband DOA estimation algorithm without array calibration is proposed, to deal with these array model errors, using the arbitrary antenna array of omnidirectional elements. By using the matrix operators that have the memory and oblivion characteristics, this algorithm can separate the incident signals effectively. Compared with other typical wideband DOA estimation algorithms based on the subspace theory, this algorithm can get robust DOA estimation with regard to position error, gain-phase error, and mutual coupling, by utilizing a relaxation technique based on signal separation. The signal separation category and the robustness of this algorithm to the array model errors are analyzed and proved. The validity and robustness of this algorithm, in the presence of array model errors, are confirmed by theoretical analysis and simulation results.     

基于自适应共轭梯度算法的高分辨率谱估计器

基于AR模型的功率谱估计是现代谱估计应用非常广泛的一种方法。通过对一全极点模型的参数估计来实现功率谱估计。提出了一种采用自适应共轭梯度算法来进行参数估计的方法。由于共轭梯度算法采用迭代运算求解Yule-Walker方程,同现有的谱估计算法相比,大大减小了谱估计算法的计算复杂度;随着自相关矩阵阶数的增大,该方法谱估计精度在小信噪比下提高显著。仿真结果表明,这种方法和基于AR模型的其它谱估计方法在不同信噪比下具有几乎相同的分辨率。因此,该谱估计算法具有重要的实用意义,有助于谱估计算法的实时实现。     

基于变换核密度估计的半参数GARCH模型

针对金融资产收益率分布呈现的尖峰、厚尾及有偏的特点,沿袭变换核密度估计的思想,提出一种广义Logistic变换,对变换后的样本应用Beta核密度估计以消除边界偏差. 模拟试验表明,该方法显著提高了对尖峰厚尾分布密度的估计精度. 继而将该方法与参数化的GARCH设定相结合,建立一种半参数GARCH模型. 该模型具有两个优点：第一,基于变换核密度估计可更加准确地估计收益率的条件分布;第二,通过迭代提高了参数估计的稳健性. 模拟试验表明,较之伪极大似然估计法和基于离散最大惩罚似然估计的半参数方法,该方法大大提高了参数估计的相对效率. 对沪深300指数的实证研究验证了本文模型的有效性.     

Performance comparison of distributed state estimation algorithms for power systems

A newly proposed distributed dynamic state estimation algorithm based on the maximum a posteriori (MAP) technique is generalised and studied for power systems. The system model involves linear time-varying load dynamics and nonlinear measurements. The main contribution of this paper is to compare the performance and feasibility of this distributed algorithm with several existing distributed state estimation algorithms in the literature. Simulations are tested on the IEEE 39-bus and 118-bus systems under various operating conditions. The results show that this distributed algorithm performs better than distributed quasi-steady state estimation algorithms which do not use the load dynamic model. The results also show that the performance of this distributed method is very close to that by the centralized state estimation method. The merits of this algorithm over the centralized method lie in its low computational complexity and low communication load. Hence, the analysis supports the efficiency and benefits of the distributed algorithm in applications to large-scale power systems.     

门限分位数自回归模型及在股市收益自相关分析中应用

门限分位数自回归模型(threshold quantile autoregressive model,简记为TQAR)是一种非线性分位数回归模型,主要用于讨论系统中的门限效应.在TQAR模型中,自回归阶数与门限值的确定等,都会影响模型分析效果.为此,本文给出模型定阶、门限值估计及门限效应检验等方法.数值模拟结果表明,TQAR模型在门限值估计、回归系数估计的有限样本表现方面都优于传统的门限均值自回归模型(threshold autoregressive model,简记为TAR)及门限均值自回归条件异方差(TAR-GARCH)模型.最后,将TQAR模型应用于中国股市收益的自相关性研究,实证结果证实收益序列的自相关性呈现出明显的门限效应和异质效应.这一发现,有助于准确刻画股市收益动态变化规律,为重新认识金融市场运行机制提供了一个实证基础.     

基于一种增量式一元线性回归模型的自适应逆控制

为受控系统提出了一种简便实用的增量式一元线性回归模型。为了跟踪快时变参数 ,提出了一种滚动多模型加权平均参数估计算法。在参数估计和系统控制的过程中运用智能技术 ,使估值更可靠 ,并形成了以基于该模型的自适应逆控制为主、常规控制为辅的多模态控制方式。仿真结果表明 ,这一控制方式对于控制非线性和时变系统非常有效。     

基于BP神经网络的GPT2w改进模型及全球精度分析

为提高全球卫星导航定位系统(global navigation satellite system,GNSS)的定位授时能力,方便用户在无法架设气象参数探测设备的条件下获得更高精度的对流层延迟估计,综述了当前主要的对流层延迟模型的发展,分析了两种空间分辨率的全球温压湿(global pressure and temperature 2 wet,GPT2w)模型在全球大地测量观测系统(global geodetic observing system, GGOS)测站处的估计精度,根据GPT2w模型的对流层延迟估计误差与气象参数估计误差的关系,提出了将实测温度与模型经验拟合气象参数相结合的策略,建立了基于反向传播神经网络的GPT2w改进模型。仿真结果表明,改进模型在2017年全球GGOS测站处对流层延迟估计精度较GPT2w模型提升近32%,且对全球其他位置估计精度同样有改进效果,改进程度与GGOS测站疏密程度有关。     

可观测度在光合作用模型性能评估中的应用

光合作用的光化学反应涉及多种无法直接测量的生理物质，通过对控制系统建模，可基于叶绿素荧光对这些生理物质量进行状态估计，但是状态估计的可靠性文献上没有涉及。基于光化学反应动力学模型，引入非线性系统的可观测度来评价状态估计的可靠性，针对由于不同状态分量的量纲不同而引起的可观测度缺乏直接可比性的问题，采用量级影响因子和对数归一化的方法消除量级的影响，以改进对模型的可观测度衡量，提出可观测度改进方案。仿真结果表明：新的可观测度指标与状态估计的误差特性基本相符，能较好地评估模型的状态估计效果。     

回报率的滤波与极大似然估计

几何布朗运动常用来描述风险资产价格的变动,其中回报率的估计是一个困难的问题.本文给出了回报率的Kalman滤波,对滤波估计与极大似然估计进行了比较分析,运用Monte Carlo技术给出了算例,结果表明Kalman滤波具有明显的优势.     

Double Penalized Semi-Parametric Signed-Rank Regression with Adaptive LASSO

In this paper, a semi-parametric regression model with an adaptive LASSO penalty imposed on both the linear and the nonlinear components of the mode is considered. The model is rewritten so that a signed-rank technique can be used for estimation. The nonlinear part consists of a covariate that enters the model nonlinearly via an unknown function that is estimated using Bsplines. The author shows that the resulting estimator is consistent under heavy-tailed distributions and asymptotic normality results are given. Monte Carlo simulations as well as practical applications are studied to assess the validity of the proposed estimation method.     

PMCMC for Term Structure of Interest Rates under Markov Regime Switching and Jumps

A parameter estimation method, called PMCMC in this paper, is proposed to estimate a continuous-time model of the term structure of interests under Markov regime switching and jumps.There is a closed form solution to term structure of interest rates under Markov regime. However, the model is extended to be a CKLS model with non-closed form solutions which is a typical nonlinear and non-Gaussian state-space model(SSM) in the case of adding jumps. Although the difficulty of parameter estimation greatly prevents from researching models, we prove that the nonlinear and non-Gaussian state-space model has better performances in studying volatility. The method proposed in this paper will be implemented in simulation and empirical study for SHIBOR. Empirical results illustrate that the PMCMC algorithm has powerful advantages in tackling the models.     

AR(1)-MA(1)模型的矩估计及其渐近分布

利用矩方法 ,给出了双重时间序列 AR( 1) - MA ( 1)模型的矩估计 ;并证明了该估计的渐近正态性.