基于微博情绪信息的股票市场预测

行为金融理论的研究表明,股票投资者在进行投资决策时,容易受到自身的因素如情绪与心理因素的影响.以行为金融理论为依据,作出基本假设:微博情绪信息反映的社会整体情绪倾向能够影响并预测股票市场整体价格走势的变化.实证过程包括抓取新浪微博数据并进行预处理,生成情绪倾向时间序列,通过格兰杰因果关系检验上证指数时间序列与情绪倾向时间序列间的相关关系,建立支持向量机模型预测股票市场价格的变化采验证假设的正确性.实验结果显示加入微博情绪信息的预测模型能够获得更高的准确率,进而证明了本文所作假设的正确性.     

一致性分数阶优化灰色模型及其应用

传统的分数阶灰色预测模型在时间序列预测中具有较好的适应性和预测的有效性,但其累加和差分计算式比较复杂。一致性分数阶累加相对于一般的分数阶累加,形式更简单,更便于计算和理论推导。为了提高模型的适应性和预测能力,文章在CFGM(1,1)白化方程中引入一个新的可变系数,扩大了原有白化方程的适用范围,并在此基础上构建了一致性分数阶优化灰色模型,即CFOGM(1,1)模型。最优一致性分数阶阶数和可变系数通过PSO算法最小化平均相对误差获得。将构建的模型运用到两个实例中并与其他经典的灰色预测模型进行对比,结果表明所提出的模型具有较高的拟合和预测精度。     

基于功能对等视域的法律翻译

作为一类特殊文本,法律的翻译必须将准确性置于首要位置.奈达的功能对等理论为法律翻译提供了切实可行的指导思想.文章从词汇、句子、篇章及文体四个方面探讨了功能对等理论在法律翻译中的具体应用,并得出结论：法律翻译应尽量追求原文与文本的功能对等.     

图式理论与大学英语听力

图式理论是认知心理学家用以解释理解心理过程的理论,听力理解是语言理解的最普遍形式,图式在听力理解的预测、推理过程中起着重要的作用。在大学英语听力教学中,培养学生的图式意识和图式策略是提高听力理解水平的有效途径。     

论政府效能监察体系的构建

中国在进行廉政建设的同时,也在进行政府效能监察工作。效能监察是监察机关为维护公共利益对监察对象存在的不尽职、不正确尽职、影响其他人尽职,乃至玩忽职守渎职失职等违法、违纪行为进行的监督检查活动。由于理论支撑滞后和相对经验不足,至今没有形成公众认可的、在较大范围适用的政府效能监察体系。本文依据调查资料,分析了构建当代政府效能监察体系的实践基础,揭示了政府效能监察体系的内容结构,预测了政府效能监察的发展趋势,提出了健全和完善效能监察体系的措施。     

Dimensionality reduction approach to multivariate prediction

The authors consider dimensionality reduction methods used for prediction, such as reduced rank regression, principal component regression and partial least squares. They show how it is possible to obtain intermediate solutions by estimating simultaneously the latent variables for the predictors and for the responses. They obtain a continuum of solutions that goes from reduced rank regression to principal component regression via maximum likelihood and least squares estimation. Different solutions are compared using simulated and real data.     

Reml and best linear unbiased prediction in state space models

This article takes a hierarchical model approach to the estimation of state space models with diffuse initial conditions. An initial state is said to be diffuse when it cannot be assigned a proper prior distribution. In state space models this occurs either when fixed effects are present or when modelling nonstationarity in the state transition equation. Whereas much of the literature views diffuse states as an initialization problem, we follow the approach of Sallas and Harville (1981,1988) and incorporate diffuse initial conditions via noninformative prior distributions into hierarchical linear models. We apply existing results to derive the restricted loglike-lihood and appropriate modifications to the standard Kalman filter and smoother. Our approach results in a better understanding of De Jong's (1991) contributions. This article also shows how to adjust the standard Kalman filter, the fixed inter- val smoother and the state space model forecasting recursions, together with their mean square errors, for he presence of diffuse components. Using a hierarchical model approach it is shown that the estimates obtained are Best Linear Unbiased Predictors (BLUP).     

Transformation and Smoothing in Sample Survey Data

Abstract. We consider model‐based prediction of a finite population total when a monotone transformation of the survey variable makes it appropriate to assume additive, homoscedastic errors. As the transformation to achieve this does not necessarily simultaneously produce an easily parameterized mean function, we assume only that the mean is a smooth function of the auxiliary variable and estimate it non‐parametrically. The back transformation of predictions obtained on the transformed scale introduces bias which we remove using smearing. We obtain an asymptotic expansion for the prediction error which shows that prediction bias is asymptotically negligible and the prediction mean‐squared error (MSE) using a non‐parametric model remains in the same order as when a parametric model is adopted. The expansion also shows the effect of smearing on the prediction MSE and can be used to compute the asymptotic prediction MSE. We propose a model‐based bootstrap estimate of the prediction MSE. The predictor produces competitive results in terms of bias and prediction MSE in a simulation study, and performs well on a population constructed from an Australian farm survey.     

The Role of Statistical Models in the Prediction of Tropical Cyclone Motion

Tropical cyclones (tropical storms, hurricanes, typhoons, etc.) occur over many of the earth's tropical marine areas. Responsibility for tracking and predicting the future course of these storms is assigned to one or more domestic or foreign meteorological services. These services routinely activate a number of statistical and dynamical prediction models as objective guidance preparatory to issuing official forecasts on these storms. In this article, the role of the statistical models in this process is examined.     

良导体导热系数测定实验的改进

根据良导体导热系数测定实验装置的特点，分析了该实验装置的缺陷并提出了改进意见。通过改进，不仅可以提高实验本身的精度，还可在此实验的基础上开发出新实验，增加了实验设备的用途。