基于分块的超链引导的主题搜索改进算法

通过引入“分块”这一新技术,提出了一个改进的HITS算法,即利用VIPS和分块重要性模型,分割集合中的每一个网页,并为其分配重要值,再结合内容分析法来修改HITS算法中authority网页的权值,从而提高搜索精度,使得搜索结果更接近查询者的意愿.     

基于支持向量机的模糊小波神经网络

针对高维输入小波网络的初始参数和网络结构非常复杂且计算量大的问题,提出用支持向量机(SVM)确定小波网络的初始参数和网络结构的方法。首先,使用有监督模糊聚类算法从聚类中抽取模糊规则,然后对每一个规则的后件使用支持向量机方法确定小波网络的结构和初始参数,最后采用梯度下降方法调节模糊小波网络中的参数,使得模糊小波网络输出与期望输出之间的误差较小。仿真结果表明:该算法与传统的模糊神经网络(FNN)相比显著提高了分类精度。     

基于子类划分的SVM及水声目标识别应用

针对目标类空间的可分离性特点,研究了动态聚类与支持向量机相结合的基于子类划分的支持向量机.提出了以子类中心为基点度量训练样本惩罚度的方法.在采用动态聚类将目标类划分为子类的基础上,综合考虑训练样本与所属子类的距离、子类对所属目标类的隶属度及目标类间的关系,以度量不同训练样本的惩罚度.并应用于水声目标识别中,对两类舰船目标的识别情况进行了比较,实验表明效果好于经典支持向量机.     

Nowcasting quarterly GDP growth in a monthly coincident indicator model

This paper presents an extension of the Stock and Watson coincident indicator model that allows one to include variables available at different frequencies while taking care of missing observations at any time period. The proposed procedure provides estimates of the unobserved common coincident component, of the unobserved monthly series underlying any included quarterly indicator, and of any missing values in the series. An application to a coincident indicator model for the Portuguese economy is presented. We use monthly indicators from business surveys whose results are published with a very short delay. By using the available data for the monthly indicators and for quarterly real GDP, it becomes possible to produce simultaneously a monthly composite index of coincident indicators and an estimate of the latest quarter real GDP growth well ahead of the release of the first official figures. Copyright © 2005 John Wiley & Son, Ltd.     

Market risk management of banks: implications from the accuracy of Value‐at‐Risk forecasts

This paper adopts the backtesting criteria of the Basle Committee to compare the performance of a number of simple Value‐at‐Risk (VaR) models. These criteria provide a new standard on forecasting accuracy. Currently central banks in major money centres, under the auspices of the Basle Committee of the Bank of International settlement, adopt the VaR system to evaluate the market risk of their supervised banks. Banks are required to report VaRs to bank regulators with their internal models. These models must comply with Basle's backtesting criteria. If a bank fails the VaR backtesting, higher capital requirements will be imposed. VaR is a function of volatility forecasts. Past studies mostly conclude that ARCH and GARCH models provide better volatility forecasts. However, this paper finds that ARCH‐ and GARCH‐based VaR models consistently fail to meet Basle's backtesting criteria. These findings suggest that the use of ARCH‐ and GARCH‐based models to forecast their VaRs is not a reliable way to manage a bank's market risk. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasting with leading indicators revisited

Transfer function or distributed lag models are commonly used in forecasting. The stability of a constant‐coefficient transfer function model, however, may become an issue for many economic variables due in part to the recent advance in technology and improvement in efficiency in data collection and processing. In this paper, we propose a simple functional‐coefficient transfer function model that can accommodate the changing environment. A likelihood ratio statistic is used to test the stability of a traditional transfer function model. We investigate the performance of the test statistic in the finite sample case via simulation. Using some well‐known examples, we demonstrate clearly that the proposed functional‐coefficient model can substantially improve the accuracy of out‐of‐sample forecasts. In particular, our simple modification results in a 25% reduction in the mean squared errors of out‐of‐sample one‐step‐ahead forecasts for the gas‐furnace data of Box and Jenkins. Copyright © 2003 John Wiley & Sons, Ltd.     

Selection of Value‐at‐Risk models

Value‐at‐Risk (VaR) is widely used as a tool for measuring the market risk of asset portfolios. However, alternative VaR implementations are known to yield fairly different VaR forecasts. Hence, every use of VaR requires choosing among alternative forecasting models. This paper undertakes two case studies in model selection, for the S&P 500 index and India's NSE‐50 index, at the 95% and 99% levels. We employ a two‐stage model selection procedure. In the first stage we test a class of models for statistical accuracy. If multiple models survive rejection with the tests, we perform a second stage filtering of the surviving models using subjective loss functions. This two‐stage model selection procedure does prove to be useful in choosing a VaR model, while only incompletely addressing the problem. These case studies give us some evidence about the strengths and limitations of present knowledge on estimation and testing for VaR. Copyright © 2003 John Wiley & Sons, Ltd.     

VAMATEX C401S型剑杆织机织造线绢织物上机工艺的探讨

分析了在VAMATEX C401S型剑杆织机织造线绢织物的主要疵点和对质量影响较大的主要原因,介绍了优选工艺的路径和方法,并阐述了主要因素与经向断头、布面外观和疵点的关系,讨论了如何提高产品质量的工艺措施.     

Garch forecasting performance under different distribution assumptions

This paper investigates the forecasting performance of the Garch (1, 1) model when estimated with NINE different error distributions on Standard and Poor's 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of volatility from intra‐day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. Copyright © 2006 John Wiley & Sons, Ltd.     

基于SVM的一种医疗数据分析模型

基于SVM分类算法和Web服务框架,提出了一种医疗数据分析与疾病预测模型,改进了医疗数据分析系统与医院数据库之间的数据传输协议.采用该模型与长春某三级甲等医院合作,获取了总共1 695条病人电子病历数据与病人疾病信息作为实验数据,并在医疗数据分析系统中进行数据挖掘分析.通过数据条数的变化和对属性的控制来测试设计的数据分析模型和改进的数据传输协议的传输效率.实验表明,在传输数据之前对数据进行预处理并且通过特征选择算法进行降维处理有助于提高整个系统的医疗数据传输效率和预测准确度.