一种基于神经网络和决策树的信用评估新方法

分析了数据挖掘在国内外金融领域的应用及研究现状,提出了一种基于神经网络和决策树相结合的信用评估新方法。该方法通过RBF神经网络,进行条件属性裁减,并利用决策树抽取出评估规则。此方法利用神经网络的“黑箱”工作特性,选择重要条件属性,并利用决策树自动生成评估规则,大大提高了信用评估的效率和客观性。     

基于数据挖掘的客户关系分析评价系统

分析功能的深化是客户关系管理(CRM)的一大发展趋势,应用数据挖掘技术建立的CRM分析评价系统,可以对客户的行为以及市场趋势进行有效的分析,为企业的各种决策提供定性和定量的依据。该系统主要模块包括:客户细分模块、客户行为分析模块和市场趋势分析模块。以上各个模块涉及了企业关心的各种客户行为及市场行为,通过各个模块的分析,企业可以辨认出高端客户,理解各种客户行为模式,并掌握市场发展趋势以保证企业在激烈的竞争中立于不败之地。     

悬浮液进样石墨炉原子吸收光谱法测定土壤样品中微量硒

采用悬浮液进样石墨炉原子吸收光谱法测定土壤样品中微量硒，选用钯－抗坏血酸为基体改进剂、锆涂覆石墨管。以标准物质制作校正曲线等一系列措施，有效消防共存元素的干扰，并保证了标准系列与土壤样品的原子化条件的一致性，获得了满意的分析结果。     

Nyquist采样频率下的频谱失真与采样定理

本文通过在Nyquist采样率下对信号的频谱是否发生失真，发生了怎样的失真的讨论，分析了在进行离散傅立叶变换（DFT）时，采样定理在其边界条件下进行信号处理时存在的问题。展示了在该条件下频谱发生严重失真的形式和程度，最后从无失真地恢复时域信号x(t）的角度讨论了DFT意义下采样定理的表述。     

化学学习动机的调查分析

作者运用教育心理学的课堂成就动机理论结合化学教学实际 ,选取了 14种化学学习动机进行问卷调查 ,并进行总体统计分析和抽样统计分析 ,得出了这样的结论。化学教师可以在教学中有效地培养 ,激发和强化学生的化学学习动机 ,提高教与学的效率 ,更好地培养学生的创新精神和实践能力。     

Missing data and forecasting in multivariate time series: An application of the common components dynamic linear model

Summary The paper deals with missing data and forecasting problems in multivariate time series making use of the Common Components Dynamic Linear Model (DLMCC), presented in Quintana (1985), and West and Harrison (1989). Some results are presented and discussed: exploiting the correlation between series, estimated by the DLMCC, the paper shows as it is possible to update state vector posterior distributions for the unobserved series. This is realized on the base of the updating of the observed series state vectors, for which the usual Kalman filter equations can be applied. An application concerning some Italian private consumption series provides an example of the model capabilities.     

Sampling Strategies for Toxic Air Contaminants

Annual concentrations of toxic air contaminants are of primary concern from the perspective of chronic human exposure assessment and risk analysis. Despite recent advances in air quality monitoring technology, resource and technical constraints often impose limitations on the availability of a sufficient number of ambient concentration measurements for performing environmental risk analysis. Therefore, sample size limitations, representativeness of data, and uncertainties in the estimated annual mean concentration must be examined before performing quantitative risk analysis. In this paper, we discuss several factors that need to be considered in designing field-sampling programs for toxic air contaminants and in verifying compliance with environmental regulations. Specifically, we examine the behavior of SO2, TSP, and CO data as surrogates for toxic air contaminants and as examples of point source, area source, and line source-dominated pollutants, respectively, from the standpoint of sampling design. We demonstrate the use of bootstrap resampling method and normal theory in estimating the annual mean concentration and its 95% confidence bounds from limited sampling data, and illustrate the application of operating characteristic (OC) curves to determine optimum sample size and other sampling strategies. We also outline a statistical procedure, based on a one-sided t-test, that utilizes the sampled concentration data for evaluating whether a sampling site is compliance with relevant ambient guideline concentrations for toxic air contaminants.     

Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time?

This article introduces a new model for transaction prices in the presence of market microstructure noise in order to study the properties of the price process on two different time scales, namely, transaction time where prices are sampled with every transaction and tick time where prices are sampled with every price change. Both sampling schemes have been used in the literature on realized variance, but a formal investigation into their properties has been lacking. Our empirical and theoretical results indicate that the return dynamics in transaction time are very different from those in tick time and the choice of sampling scheme can therefore have an important impact on the properties of realized variance. For RV we find that tick time sampling is superior to transaction time sampling in terms of mean-squared-error, especially when the level of noise, number of ticks, or the arrival frequency of efficient price moves is low. Importantly, we show that while the microstructure noise may appear close to IID in transaction time, in tick time it is highly dependent. As a result, bias correction procedures that rely on the noise being independent, can fail in tick time and are better implemented in transaction time.     

Optimal sampling for repeated binary measurements

The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k. The implementation follows the approach of Quintana & Müller (2004), which uses a Dirichlet process prior for the mixture.     

中国各地区医疗卫生服务的生产效率分析

医疗卫生是与国民密切相关的一个问题，所以医疗卫生服务的生产效率如何倍受人们的关注。运用DEA模型可以对中国各地区的医疗卫生服务生产效率进行研究，分析不同地区效率高低的原因。结果显示，虽然总体上中国的医疗卫生服务的生产效率处于一个较低水平，但地区之间还是存在着很大的区别，并且地区间即便都是高效率或者低效率的地区，其投入和产出水平也不尽相同。