润滑油脂中抗氧化添加剂作用机理

本文针对润滑油及润滑脂中主要添加剂──抗氧化添加剂的作用机理，从化学反应角度进行理论探讨，并进行了实例分析．     

Prediction theory for autoregressivemoving average processes

This paper reviews statistical prediction theory for autoregressive-moving average processes wing techniques developed in control theory. It demonstrates explicitly the connectioluns between the statistical and control theory literatures. Both the forecasting problem and the Single extraction problem am considered, udng linear least squares methods. Whereas the classical Statistical theory developed by Wiener and Kolmogomv is restricted to stationary stochaotic processes, the recursive techniques known as the Kalman filter are shown to provide a satisfactory treatment of the difference-stationary care and other more general cases. Complete results for non-invertible moving averages are also obtained.     

一种新的SAR图像Speckle滤波算法

详细分析了合成孔径雷达图像中相干斑的统计特性，提出了一种具有窗口自选功能的自适应相干斑滤波算法，并将该算法用于Ｓｅａｓａｔ－ＡＳＡＲ图像的滤波处理；并将滤波结果与Ｌｅｅ算法和Ｏｄｄｙ算法作了对比试验，其结果表明滤波性能优于Ｌｅｅ算法和Ｏｄｄｙ算法。     

Prediction and forecasting in linear models with measurement error

We consider measurement error models within the time series unobserved component framework. A variable of interest is observed with some measurement error and modelled as an unobserved component. The forecast and the prediction of this variable given the observed values is given by the Kalman filter and smoother along with their conditional variances. By expressing the forecasts and predictions as weighted averages of the observed values, we investigate the effect of estimation error in the measurement and observation noise variances. We also develop corrected standard errors for prediction and forecasting accounting for the fact that the measurement and observation error variances are estimated by the same sample that is used for forecasting and prediction purposes. We apply the theory to the Yellowstone grizzly bears and US index of production datasets.     

Bayesian Analysis of an Unobserved-Component Time Series Model of GDP With Markov-Switching and Time-Varying Growths

We propose an unobserved-component time series model of gross domestic product that includes Markov switching as an unobserved component. In addition to a trend component, the model has two time-varying drift components. One drift represents the expected rate of growth during recession; the other drift represents the expected rate during expansion. Estimates indicate a substantial decline in the latter annual rate for the United States from 6.4% in 1950 to 3.6% by 1990. We have employed weak priors based on prewar data. The estimation makes use of the Gibbs sampler and the Metropolis algorithm.     

Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns

A stochastic volatility model may be estimated by a quasi-maximum likelihood procedure by transforming to a linear state-space form. The method is extended to handle correlation between the two disturbances in the model and applied to data on stock returns     

公共计算机实验室软件系统管理与实践

通过对河北科技师范学院公共计算机实验室课程设置、机房安排、软件划分等内容的合理规划,软件的安装、测试、优化和完善等方面技术手段的应用,硬盘保护卡功能的有效运用以及网络技术的综合使用及多种手段的优化比较,构建了完整的高校公共计算机实验室软件系统管理模式,实现了对高校公共机房实验室设备的充分利用和高效维护,以及对公共机房软件系统的科学管理。     

An alternative derivation of the Kalman filter using the quasi-likelihood method

The Kalman filter gives a recursive procedure for estimating state vectors. The recursive procedure is determined by a matrix, so-called gain matrix, where the gain matrix is varied based on the system to which the Kalman filter is applied. Traditionally the gain matrix is derived through the maximum likelihood approach when the probability structure of underlying system is known. As an alternative approach, the quasi-likelihood method is considered in this paper. This method is used to derive the gain matrix without the full knowledge of the probability structure of the underlying system. Two models are considered in this paper, the simple state space model and the model with correlated between measurement and transition equation disturbances. The purposes of this paper are (i) to show a simple way to derive the gain matrix; (ii) to give an alternative approach for obtaining optimal estimation of state vector when underlying system is relatively complex.     

LSA和MD5算法在垃圾邮件过滤系统的应用研究

随着对垃圾邮件问题的普遍关注,针对目前邮件过滤方法中存在着的语义缺失现象和处理群发型垃圾邮件低效问题,提出一种基于潜在语义分析(LSA)和信息-摘要算法5(MD5)的垃圾邮件过滤模型。利用潜在语义分析标注垃圾邮件中潜在特征词,从而在过滤技术中引入语义分析;利用MD5在LSA分析基础上,对群发型垃圾邮件生成"邮件指纹",解决过滤技术在处理群发型垃圾邮件中低效的问题。结合该模型设计了一个垃圾邮件过滤系统。采用自选数据集对文中设计的系统进行测试评估,经与Nave Bayes算法过滤器进行比较,证明该方法在垃圾邮件过滤上优于Nave Bayes方法,实验结果达到了预期的效果,验证了该方法的可行性、优越性。     

Small domain estimation by empirical bayes and kalman filtering procedures - a case study

An application of empirical Bayes and Kalman filtering tecniques is reported, using live data from Indian Statistical Institute (ISI), Calcutta . to illustrate how initial small domain estimators may be vastly improved upon. A stratified two stage sampling procedure is adopted, allowing selection of first stage units with unequal probabilities but of second stage units with equal probabilities. Standard design-based estimators for domain totals are initialized based on domain specific survey data alone. Strength is then borrowed across domains and from past surveys. The resulting gains in efficacy are numlerically demonstrated, through replicated sampling from official records.