随机波动模型估计方法研究进展

本文对随机波动模型估计方法取得的进展进行了综述与简评 ,并提出了今后值得研究的一些相关问题。

随机解释变量的模型及其估计方法的研究

本文分析了随机性解释变量存在的可能性及其后果 ,其次对解释变量的随机性进行检验 ,最后讨论解释变量为随机变量的模型估计方法 ,包括正交回归估计法、正反向回归估计法、最小特征值估计法和工具变量估计法。

利用Monte-Carlo方法模拟比较动态平行数据模型参数的估计方法——最小二乘与工具变量估计方法

总结了动态平行数据模型的固定效应与随机效应模型的最小二乘估计 (OLS)与工具变量估计(Tool)方法 .并利用 Monte-

宏观交通流模型参数标定方法

为了提高交通流模型整体估计精度,对交通流模型参数估计方法进行了研究.针对密度-速度、密度-流量以及速度-流量模型之间的关联性以及交通流观测数据分布特征对模型估计精度的影响,提出了联合模型参数估计方法,并给出了联合模型参数估计优化目标函数的表达形式及约束条件.以Castillo-Benítez和Van Aerde模型为例,基于北京市二环快速路实测数据对联合模型参数估计方法可行性及参数估计效果进行了验证;构建了加权判定系数,并结合平均绝对百分比误差(mean absolute percentage error,MAPE)和平方根误差(root mean squared error,RMSE)评价联合模型估计效果.结果表明,对Castillo-Benítez模型而言,由单一模型计算的速度、流量估计MAPE分别是19.8%和18.7%,基于联合模型计算的速度、流量MAPE分别下降为10.0%和10.0%,模型总体判决系数由0.913变化为0.910;对Van Aerde模型而言,由单一模型计算的密度、流量估计MAPE分别为16.4%和16.3%,基于联合模型计算的密度、流量MAPE分别为14.2%和14.2%,模型总体判决系数由0.732变为0.749.

多模型UKF方法及其在故障诊断中的应用

当系统的非线性行为较强时,扩展卡尔曼滤波器对系统状态变量估计的性能将大幅下降;针对传统的扩展多模型自适应估计方法 (EMMAE)的不足,文章提出了一种新的基于无迹卡尔曼滤波(UKF)和多模型自适应估计相结合的故障诊断方法;首先利用UKF能更好的逼近状态方程的非线性特性,然后利用改善后的残差质量提高故障隔离的效果;仿真结果表明,传统的故障诊断方法相比,该方法能快速准确地辨识出系统故障。

多项Probit模型中回归系数的逆回归估计

本文将多项Probit模型推广到更一般的形式,研究了推广的多项Probit模型的逆回归性质,给出了回归系数的逆回归估计方法,并证明了在满足一些条件时估计是渐近正态的.模拟表明逆回归估计方法有良好的表现.

城市人口时空分布估计研究进展

城市人口是构成城市的社会主体,是城市发展中最为活跃的因素。城市人口时空分布是城市管理需要掌握的重要信息。正确、精细化的城市人口分布数据对于城市运行与规划、城市经济发展和人民生活具有极为重要的意义,因此城市人口时空分布估计是城市地理学需要解决和研究的热点问题之一。本文以城市人口时空分布估计的关键点为核心,从以下几个方面展开综述:①空间分布单元划分方法,即城市人口分布的空间划分方式;②主要的模型和方法,从模型思想发展过程和估计对象的角度总结了6类方法并进行详细阐述;③估计结果在城市发展中的主要应用。在此基础上,本文指出了目前人口时空分布研究在空间单元构建、建模数据、建模思想和结果验证上存在的问题,同时探讨了未来的研究方向。

国际贸易波动——理论综述及评价

单方程估计方法 经典的国际贸易和金融理论中,对进出口规模的决定问题有着详实的讨论.其中大多数文献均是从进出口需求弹性的角度人手,将进出口的波动分为价格效应和收入效应两个方面,然后通过估计进出口和实际汇率、相对收入的单一计量方程式来确定两种效应的影响.类似讨论可参见Goldstein和Khan(1985),Hooper和Marquez(1995),Clarida(1994)等等.当然,通常所使用的计量模型,在具体设定上多少都区别于从优化模型的一阶条件中所得到的结果.这样做是为了能更多的反应现实经济中的一些特殊情况.不过,类似讨论的一个共同特点是仅仅考虑各种对贸易行为的直接影响因素,而忽略其它可能的间接影响因素,这实际是90年代国际宏观经济学更多地使用结构模型估计方法的原因.

Spatiotemporal distribution model for zinc electrowinning process and its parameter estimation

This paper focuses on the distributed parameter modeling of the zinc electrowinning process(ZEWP)to reveal the spatiotemporal distribution of concentration of zinc ions(CZI)and sulfuric acid(CSA)in the electrolyte.Considering the inverse diffusion of such ions in the electrolyte,the dynamic distribution of ions is described by the axial dispersion model.A parameter estimation strategy based on orthogonal approximation has been proposed to estimate the unknown parameters in the process model.Different industrial data sets are used to test the effectiveness of the spatiotemporal distribution model and the proposed parameter estimation approach.The results demonstrate that the analytical model can effectively capture the trends of the electrolysis reaction in time and thus has the potential to implement further optimization and control in the ZEWP.

Video Scene Invariant Crowd Density Estimation Using Geographic Information Systems

Crowd density is an important factor of crowd stability.Previous crowd density estimation methods are highly dependent on the specific video scene.This paper presented a video scene invariant crowd density estimation method using Geographic Information Systems(GIS) to monitor crowd size for large areas.The proposed method mapped crowd images to GIS.Then we can estimate crowd density for each camera in GIS using an estimation model obtained by one camera.Test results show that one model obtained by one camera in GIS can be adaptively applied to other cameras in outdoor video scenes.A real-time monitoring system for crowd size in large areas based on scene invariant model has been successfully used in 'Jiangsu Qinhuai Lantern Festival,2012'.It can provide early warning information and scientific basis for safety and security decision making.

Numerical method for estimation of kinematical parameters for articulated rovers on loose rough terrain

Based on the study of passive articulated rover,a complete suspension kinematics model from wheel to inertial reference frame is presented,which uses D-H method of manipulator and presentation with Euler angle of pitch,roll and yaw.An improved contact model is adopted aimed at the loose and rough lunar terrain.Using this kinematics model and numerical continuous and discrete Newton's method with iterative factor,the numerical method for estimation of kinematical parameters of articulated rovers on loose and rough terrain is constructed.To demonstrate this numerical method,an example of two torsion bar rocker-bogie lunar rover with eight wheels is presented.Simulation results show that the numerical method for estimation of kinematical parameters of articulated rovers based on improved contact model can improve the precision of kinematical estimation on loose and rough terrain and decrease errors caused by contact models established based on general hypothesis.

Estimating van Genuchten Model Parameters of Undisturbed Soils Using an Integral Method

The van Genuchten model is the most widely used soil water retention curve （SWRC） model. Two undisturbed soils （clay and loam） were used to evaluate the accuracy of the integral method to estimate van Genuchten model parameters and to determine SWRCs of undisturbed soils. SWRCs calculated by the integral method were compared with those measured by a high speed centrifuge technique. The accuracy of the calculated results was evaluated graphically, as well as by root mean square error （RMSE）, normalized root mean square error （NRMSE） and Willmott＇s index of agreement （1）. The results obtained from the integral method were quite similar to those by the centrifuge technique. The RMSEs （4.61 ×10^-5 for Eum-Orthic Anthrosol and 2.74 × 10^-4 for Los-Orthic Entisol） and NRMSEs （1.56 × 10^-4 for Eum- Orthic Anthrosol and 1.45 ×10^-3 for Los-Orthic Entisol） were relatively small. The 1 values were 0.973 and 0.943 for Eum-Orthic Anthrosol and Los-Orthic Entisol, respectively, indicating a good agreement between the integral method values and the centrifuge values. Therefore, the integral method could be used to estimate SWRCs of undisturbed clay and loam soils.

Development of a mass model in estimating weight-wise particle size distribution using digital image processing

Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.

Development of Bayesian Network Models for Risk-Based Ship Design

In the past fifteen years, the attention of ship safety treatment as an objective rather than a constraint has started to sweep through the whole maritime industry. The risk-based ship design （RBD） methodology, advocating systematic integration of risk assessment within the conventional design process has started to takeoff. Despite this wide recognition and increasing popularity, important factors that could potentially undermine the quality of the results come from both quantitative and qualitative aspects during the risk assessment process. This paper details a promising solution by developing a formalized methodology for risk assessment through effective storing and processing of historical data combined with data generated through first-principle approaches. This method should help to generate appropriate risk models in the selected platform （Bayesian networks） which can be employed for decision making at design stare.

Parameter Estimations of the TFR Model Under Exponential Distribution at a Normal Stress

The TFR（Tampered Failure Rate） model was proposed by Bhattacharyya and Soejoeti（1989） for step-stress accelerated life tests, On step-stress completely accelerated test occasions, the paper gives a method of estimating parameters under a normal stress.

Research on Preventive Maintenance Strategy of Simulator Based on Reliability Theory

Based on the theory of reliability, this paper studies the preventive maintenance strategy of the simulator with incomplete maintenance, and establishes the preventive maintenance model with the aim of simulating the maintenance cost and simulating machine availability. The main contents of this paper are as follows： Firstly, this paper introduces the background and significance of this paper, expatiates the relevant concepts and mathematical foundation of simulator maintenance related theory and reliability maintenance thought, simulator performance degradation law, Weibull parameter estimation method, As the center of the maintenance （RCM） theory of system maintenance decision analysis.

Estimation of Number Of Small Cattle Through ARIMA Models in Turkey

In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series was formed using the data on the number of sheep and goats belonging to the period between 1930 and 2014 in Turkey It was determined through autocorrelation function graphic that the series weren＇t stationary at first, but they became stationary after their first difference were calculated. A stagnancy test was performed through extended Dickey-Fuller test. So as to determine the suitability of the model, it was reviewed if autocorrelation and partial autocorrelation graphs were white noise series and also the results of Box-Ljung test were reviwed. Through the ＂tested models, the model estimations, of which parameter estimates were significant and Akaike information criterion （AIC） was the smallest, were performed. The most appropriate model in terms of both the number of sheep and goats is first-level integrated moving average model stated as ARIMA（0,1,1）. In this model, it was estimated that there would be an increase in the number of sheep and goats in Turkey between the years of 2015 and 2020, however, the increase in the number of sheep would be more than the increase in the number of goats.

Simulating Particle Swarm Optimization Algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

This paper present a simulation study of an evolutionary algorithms, Particle Swarm Optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇obj.fun＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

The variation of the estimated GPS instrumental bias and its possible connection with ionospheric variability

Based on the analysis of the satellite DCB data estimated by our method and the Center for Orbit Determination in Europe(CODE)from 1999 to 2011,the features of the temporal variation of differential code biases(DCB)are studied.Summarily,there are three types of variations in DCB on different time scales.The first one is the day-to-day variation that exhibits more obviously in solar maximum years.The second one is the variation with about one year periodic variation that behaves more obviously from 1999 to 2004.The last one is the monotonously descending tendency from 1999 to 2010.Considering the basic ionospheric approximation in DCB estimation method,the features of the variability of the ionospheric morphology from 1999to 2010 are also displayed based on the ionospheric characteristic parameters.It can be concluded that the day-to-day and annual variation of the estimated global positioning system(GPS).DCB is related to the ionospheric variability.The variation of DCBs on solar cycle time scale includes the real hardware DCBs and pseudo-DCBs induced by ionospheric variation.No doubt,these kinds of"pseudo"variations of DCB will affect the precision of ionospheric total electron content(TEC)derived from the GPS data.In addition,this study is helpful for evaluating the influence of ionospheric weather on TEC derivation and is also useful for developing one estimation method of DCB with more stability and precision through introducing a more practical ionospheric model.

Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data

This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric expo- nential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distri- bution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.