误差项为指数白噪声的平稳AR(1)过程的贝叶斯分析

本文研究了一类误差项为指数白噪声的平稳自回归模型的参数估计问题.利用贝叶斯方法,获得了参数的后验分布及在平方损失下的贝叶斯估计结果,推广了Turkman的结果.     

二维指数信号模型中参数Bootstrap估计的强相合性

本文主要研究了在统计信号处理当中具有广泛应用的二维带白噪声指数信号模型中参数估计的Bootstrap逼近, 借助于回归模型中Bootstrap逼近的构造方法, 给出了二维指数信号模型参数的自助估计, 并证明了自助估计具有强相合性. 最后采用了Monte-Carlo法对所提的方法进行随机模拟, 模拟的结果表明当噪声不服从正态分布时, Bootstrap方法的估计效果优于最小二乘估计.     

二叉树模型的参数估计

二叉树模型是期权定价中被广泛使用的模型之一, 其参数估计对于期权定价具有重要影响. 本文给出了一种二叉树模型参数估计方法, 该方法克服了二叉树模型经典参数估计方法的缺陷, 特别地, 消除了主观概率设定对参数估计的影响.     

Autoregression Model of Time Series with Matrix Cross-Section Data北大核心CSCD

矩阵型截面数据时间序列的优点在于可以同时刻画多个对象的多个属性.本文重点研究了矩阵型截面数据时间序列的自回归模型,给出了该模型的参数估计、模型识别、白噪声检验三个方面的理论结果.最后再利用矩阵型截面数据时间序列自回归模型,对两支银行股的日收益率序列和日成交量变化率序列进行建模分析.     

关于回归模型的参数估计效率

本文讨论回归模型的参数估计效率。本文说明了现有线性回归模型的参数估计效率的下界与真实的参数估计效率在很多情况下相差较大,而且这种下界对于实测数据处理很难得到精确值。本文给出了估算参数估计效率的仿真方法。理论分析表明,该方法给出的参数估计效率的估计较现有的下界估计更合理;仿真和实算结果表明,对于一大类线性和非线性回归模型,该方法给出的回归模型的参数估计效率的估计更接近模型参数估计效率的真值。     

基于人工蜂群算法的非线性系统模型参数估计

将人工蜂群算法用于非线性系统模型的参数估计,通过对谷氨酸菌体生长模型的参数估计进行验证,并与人工神经网络、遗传算法和微粒群算法的优化结果进行了比较.仿真试验结果表明:人工蜂群算法对非线性系统模型的参数估计精度高于人工神经网络、遗传算法和微粒群算法的参数估计精度,为非线性系统模型参数估计提供了一种有效的途径.     

基于贝叶斯推理的突发水污染事件水质预测模型参数估计

参数的精确性是准确构建突发水污染事件水质预测模型的前提与保障。论文首先根据有限差分法和贝叶斯推理构建水质预测模型参数估计模型,然后通过Metropolis-Hasting抽样方法得到较为合理的参数,最后以发生在某段明渠的突发水污染事件为例,讨论了恒定流与非恒定流两种情景下不同观测噪声对参数估计结果的影响,并与采用有限差分-单纯形法得到的结果进行对比。结果表明:有限差分-贝叶斯方法具有较强的适用性和良好的抗噪声能力,采用该方法能获得较高精度的参数值。该研究为突发污染事件预测模型的构建提供一条新途径。     

基于增广的广义卡尔曼滤波模型的高精度参数估计

针对捕食模型、利用已知观测数据、在非线性最小二乘准则下,建立了基于增广的广义卡尔曼滤波模型来解决噪声背景下的高精度参数估计问题,并予以了验证.提出以相平面方程为约束的初值搜索算法,利用Matlab优化工具箱、深度搜索和剪枝加速等技术来提高搜索速度.还建立了二重规划模型以解决观测时间有误差时的高精度估计问题,并对时间误差作了正态分布检验.     

参数估计在最优化模型中的有效性及修正

证明了在涉及随机变量的最优化模型中,如果将参数估计和最优化求解过程分离,实际得到的最优化结果必然低于理论值,即参数估计在最优化模型中总是造成低效的结果。然后给出了两种可行的参数估计修正方法。     

离散时间随机系统参数估计的强一致性

对噪声具有实际重要性的一类线性随机控制系统,本文对由随机梯度算法给出的参数估计得到了一个使它为强一致的充分必要条件.同时对 ARMA 噪声情形,证明这个条件比熟知的持续激励条件为弱.     

有缺失数据的2×2×2列联表的参数估计

本文讨论了缺失数据的 2× 2× 2列联表的参数估计问题 ,通过给出适当的模型和观测数据模式 ,我们给出了各列联表各细胞参数的估计 ,并且证明这些估计是G -Markov的     

广义Fuzzy逻辑回归模型的最大Fuzzy积分估计

以两个Fuzzy集的平均相容度为基础,给出广义Fuzzy逻辑回归模型的未知参数的最大Fuzzy积分估计,这种估计能给了近似地构造广义Fuzzy函数及求广义Fuzzy关系方程的近似解的一种简便方法。     

再论逆自相关函数自回归估计的渐近性质（Ⅰ）：强收敛速度

本文在一组相当广泛的条件下,证明了线性平稳时间序列逆自相关函数自回归估计的强收敛速度,讨论这一估计在MA模型估计中的应用,获得了参数估计的强收敛速度和阶的强相容估计.     

潜周期模型中频率变点的估计

本文用逐段计算周期图的办法研究了带有频率交点的潜周期模型估计问题，给出了交点数目、位置和潜频率的强相合估计．数值模拟表明本文方法对变点个数和潜频率估计很好，但是要准确估计交点位置需要较大样本量，估计对于噪声水平较高情况仍然有效。     

Modelado numérico del proceso de soldadura FSW incorporando una técnica de estimación de parámetros

Numerical models of heat transfer and fluid flow used in the simulation of the friction-stir welding (FSW) process have contributed to the understanding of the process. However, there are some input model parameters that cannot be easily determined from fundamental principles or the welding conditions. As a result, the model predictions are not always in agreement with experimental results. In this work, the Levenberg-Marquardt (LM) method is used in order to perform a non-linear estimation of the unknown parameters present in the heat transfer and fluid flow models, by adjusting the temperatures results obtained with the models to temperature experimental measurements. These models are implemented in a general-purpose software that uses a numerical formulation developed from the finite element method (FEM). The unknown parameters are: the friction coefficient and the amount of adhesion of material to the surface of the tool, the heat transfer coefficient on the bottom surface and the amount of viscous dissipation converted into heat. The obtained results show an improvement in the numerical model predictions from the incorporation of parameter estimation techniques.     

Estimating structural credit risk models when market prices are contaminated with noise

In this paper, sequential estimation on hidden asset value and model parameter estimation is implemented under the Black–Cox model. To capture short‐term autocorrelation in the stock market, we assume that market noise follows a mean reverting process. For estimation, Bayesian methods are applied in this paper: the particle filter algorithm for sequential estimation of asset value and the generalized Gibbs and multivariate adapted Metropolis methods for model parameters estimation. The first simulation study shows that sequential hidden asset value estimation using both option price and equity price is more efficient than estimation using equity price alone. The second simulation study shows that, by applying the generalized Gibbs sampling and multivariate adapted Metropolis methods, model parameters can be estimated successfully. In an empirical analysis, the stock market noise for firms with more liquid stock is estimated as having smaller volatility. Copyright © 2015 John Wiley & Sons, Ltd.     

三参数对数正态分布的参数估计

本文讨论了三参数对正态分布的参数估计问题，指出了最小二乘估计存在的问题，并提出了两种修改的最小二乘估计，模拟研究表明，这种估计较以前提出的估计有一定的优点。     

Bayesian Monte Carlo estimation for profile hidden Markov models

Hidden Markov models are used as tools for pattern recognition in a number of areas, ranging from speech processing to biological sequence analysis. Profile hidden Markov models represent a class of so-called “left–right” models that have an architecture that is specifically relevant to classification of proteins into structural families based on their amino acid sequences. Standard learning methods for such models employ a variety of heuristics applied to the expectation-maximization implementation of the maximum likelihood estimation procedure in order to find the global maximum of the likelihood function. Here, we compare maximum likelihood estimation to fully Bayesian estimation of parameters for profile hidden Markov models with a small number of parameters. We find that, relative to maximum likelihood methods, Bayesian methods assign higher scores to data sequences that are distantly related to the pattern consensus, show better performance in classifying these sequences correctly, and continue to perform robustly with regard to misspecification of the number of model parameters. Though our study is limited in scope, we expect our results to remain relevant for models with a large number of parameters and other types of left–right hidden Markov models.     

A class of random field memory models for mortality forecasting

This article proposes a parsimonious alternative approach for modeling the stochastic dynamics of mortality rates. Instead of the commonly used factor-based decomposition framework, we consider modeling mortality improvements using a random field specification with a given causal structure. Such a class of models introduces dependencies among adjacent cohorts aiming at capturing, among others, the cohort effects and cross generations correlations. It also describes the conditional heteroskedasticity of mortality. The proposed model is a generalization of the now widely used AR-ARCH models for random processes. For such a class of models, we propose an estimation procedure for the parameters. Formally, we use the quasi-maximum likelihood estimator (QMLE) and show its statistical consistency and the asymptotic normality of the estimated parameters. The framework being general, we investigate and illustrate a simple variant, called the three-level memory model, in order to fully understand and assess the effectiveness of the approach for modeling mortality dynamics.     

导弹武器系统费用的模糊估算模型研究

利用模糊数学的基本原理,基于模糊贴近度的概念和择近原则,在同一条件下,研究和对比在研导弹武器系统与已有导弹武器系统的相似程度,在导弹武器系统费用历史资料的基础上,根据指数平滑法的理论,建立了在早期设计阶段导弹武器系统费用的模糊预测模型。仿真实例验证了该方法的适用性和结果的可靠性。