神经网络方法在股票投资中的应用

日益膨胀的股票市场信息远超出人们的处理能力，股票价格变得越来越难以预测。神经网络方法可以模拟人工智能处理海量信息。提高对股票市场的预测水平。运用中国1998-2005年股票市场数据，利用梯度下降法拟合了一个BP神经网络模型，在实证过程中重点讨论预测过程中出现的分类标准、过抽样、过度训练等问题。认为正确运用神经网络方法可以提高预测分析效果，神经网络模型可以谨慎地作为一种股票投资分析方法加以运用。     

伦理视角中的转型期社会利益关系协调

目前,我国正处于社会经济转型期,社会物质利益分配方面矛盾凸显。社会利益失衡主要导源于竞争秩序的不完善,传统体制下的伦理规范也不能从根本上协调社会利益关系。协调转型期社会利益关系必须确立以平等竞争、扶助弱者、合理消费为核心原则的伦理规范,以实现全社会共同富裕和发展。     

Simulated real-time detection of multiple structural changes: Evidence from Japanese economic growth

An efficient treatment of practical issues on detecting multiple structural changes is presented. The efficacy of this method is examined by comparing the conventional hypothesis-testing method via comprehensive simulations and empirical applications. The method recommended is a model-selection using the Bayesian information criterion and allowing for heteroscedasticity. Empirical results show that the first structural change of Japanese economic growth occurred in 1974Q2, which was detected in 1979Q4, and that the second structural change occurred in 1992Q2, which was detected in 1998Q1. Two advantages of the model-selection method compared to the hypothesis-testing method are also discussed.     

亨普尔确证悖论及其解悖方案分析

确证悖论作为科学哲学和逻辑哲学研究中极为重要的一页,得到了逻辑学家和科学哲学家持久而热烈的关注.亨普尔是在研究等值条件和尼科德标准的基础上发现确证悖论的,他的工作产生了深远的影响.其贡献不仅在于提出了确证悖论和解决悖论的方案,还在于他的研究激发了冯·赖特、科林·豪森和皮特·厄巴赫的兴趣；使得他们分别在形式逻辑框架和贝叶斯理论的指导下,提出解决确证悖论的方案,具有重要的哲学价值.     

Covariate selection for identifying the causal effects of stochastic interventions using causal networks

Suppose that cause-effect relationships between variables can be described by a causal network corresponding to a linear structural equation model. Kuroki and Miyakawa (2003) proposed a graphical criterion for selecting covariates to identify the causal effect of a conditional intervention. In this paper, we extend Kuroki and Miyakawa (2003) graphical criterion for selecting covariates to identify the causal effect of a stochastic intervention. Since stochastic intervention is a generalization of conditional intervention, our paper makes the results of Kuroki and Miyakawa (2003) more generally applicable.     

Model averaging procedure for partially linear single-index models

This paper is concerned with model selection and model averaging procedures for partially linear single-index models. The profile least squares procedure is employed to estimate regression coefficients for the full model and submodels. We show that the estimators for submodels are asymptotically normal. Based on the asymptotic distribution of the estimators, we derive the focused information criterion (FIC), formulate the frequentist model average (FMA) estimators and construct proper confidence intervals for FMA estimators and FIC estimator, a special case of FMA estimators. Monte Carlo studies are performed to demonstrate the superiority of the proposed method over the full model, and over models chosen by AIC or BIC in terms of coverage probability and mean squared error. Our approach is further applied to real data from a male fertility study to explore potential factors related to sperm concentration and estimate the relationship between sperm concentration and monobutyl phthalate.     

The approximation of an unbiased test with a small level for the bioequivalence problem

In the bioequivalence problem. Brown. Hwang and Munk (1997) constructed an unbiased level a test and other tests which are uniformly more powerful than the two one-sided tests procedures when a iscomparatively larger. In this paper, for a small level, an unbiased test is shown to be approxirnately constructeQ lor tnis prooiem oy using tneir Metnog. ine numerical construction is also given.     

On the minimum mean squared error estimators in a regression model

In this paper we analyze the properties of two estimators oroposed by Farebrother (1975) for linear regression models.     

A monte carlo study of variable selection and inferences in a two stage random coefficient linear regression model with unbalanced data

A growth curve analysis is often applied to estimate patterns of changes in a given characteristic of different individuals. It is also used to find out if the variations in the growth rates among individuals are due to effects of certain covariates. In this paper, a random coefficient linear regression model, as a special case of the growth curve analysis, is generalized to accommodate the situation where the set of influential covariates is not known a priori. Two different approaches for seleaing influential covariates (a weighted stepwise selection procedure and a modified version of Rao and Wu’s selection criterion) for the random slope coefficient of a linear regression model with unbalanced data are proposed. Performances of these methods are evaluated by means of Monte-Carlo simulation. In addition, several methods (Maximum Likelihood, Restricted Maximum Likelihood, Pseudo Maximum Likelihood and Method of Moments) for estimating the parameters of the selected model are compared Proposed variable selection schemes and estimators are appliedtotheactualindustrial problem which motivated this investigation.     

科技英语的特点及其翻译策略

科技英语有其自身的特点,科技英语具有大量使用名词结构和被动结构,非限定动词的应用和大量使用后置定语,多使用常用句型和复合词与缩略词等特点。在分析科技英语的特点的基础上,探讨科技英语翻译的原则、标准、策略与方法。