高性能可重构DSP处理器的数据通路设计

介绍了高性能定点可重构DSP处理器的数据通路设计。该数据通路以功能强大的16位定点计算单元为基础,搭建起高速16位数据处理平台;并能以单指令流多数据流的方式灵活支持多维向量运算;通过重构的方法有效地支持了32位数据处理。     

商业银行个人金融服务的国际比较

目前,我国商业银行以代发工资、代收费等业务为突破口,已开发了近40个品种的个人金融服务业务。谁能提供更快捷、方便的金融服务,谁就会拥有更多的客户。进一步的对外开放,个人金融服务的巨大市场是中外银行竞争的焦点。个人金融服务市场从以往简单地通过银行储蓄存款获得利息并保障安全,已跳跃到支付结算、外汇买卖、临时透支、贷款融资、经营投资和综合理财等全方位、多层次的服务。这些对国内各家商业银行拓展个人金融服务提出了巨大的机遇和挑战。     

小波分析在管理数据处理中的应用

提出了一种基于小波变换的管理数据处理方法，把公司管理上的数据看成一个非平稳的时间序列，利用小渡函数将该时间序列分解到不同的频率通道上，然后将分解后的信号当作近似的平稳时间序列，用一些传统的统计方法进行预测，同时对中国足球彩票若干期的销售量数据进行了处理和预测，并将结果与实际销量以及用传统的AR模型的预测值进行了比较。     

档案信息利用服务网络化现状的思考

在社会信息化的趋势下,档案信息化已被高度关注。档案信息利用服务网络化是新形势下档案利用的一种新方式。由于诸多因素的制约,使得基于Internet平台的B S模式的网络利用服务明显滞后。本文从档案信息利用服务网络化的现状、产生的原因及对策三个方面做出分析。     

论现行大学生体质检测方法的科学性

本文通过回顾我国大学生体质检测标准的发展历程,对新颁布的大学生体质监测方法和标准的科学性进行技术分析,在此基础上,对新的体质检测方法的进一步深层次的应用进行探索。     

Olive Oil Consumption in Greece: A Microeconometric Analysis

In this paper, the factors affecting at-home demand for three types of oils and fats in Greece, with emphasis on olive oil, are investigated using the linear approximation of the Almost Ideal Demand System and family budget survey data. To overcome the econometric problem created with the existence of zero expenditure, a generalization of the two-stage Heckman procedure is employed. In order to investigate the role of self-consumption, two different samples were used. The first includes all households; the second excludes those that acquire olive oil only from own production. According to the results, there are important differences in the first stage of the decision process between the two samples. Unlike the first stage, the second stage of the decision process found no important differences between the results for the two samples.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

ASSESSING CONGRUENCEAMONG DISTANCE MATRICES: SINGLE-MALT SCOTCH WHISKIES REVISITED

A test of congruence among distance matrices is described. It tests the hypothesis that several matrices, containing different types of variables about the same objects, are congruent with one another, so they can be used jointly in statistical analysis. Raw data tables are turned into similarity or distance matrices prior to testing; they can then be compared to data that naturally come in the form of distance matrices. The proposed test can be seen as a generalization of the Mantel test of matrix correspondence to any number of distance matrices. This paper shows that the new test has the correct rate of Type I error and good power. Power increases as the number of objects and the number of congruent data matrices increase; power is higher when the total number of matrices in the study is smaller. To illustrate the method, the proposed test is used to test the hypothesis that matrices representing different types of organoleptic variables (colour, nose, body, palate and finish) in single‐malt Scotch whiskies are congruent.     

Estimation of Hazard, Density and Survivor Functions for Randomly Censored Data

Maximum likelihood estimation and goodness-of-fit techniques are used within a competing risks framework to obtain maximum likelihood estimates of hazard, density, and survivor functions for randomly right-censored variables. Goodness-of- fit techniques are used to fit distributions to the crude lifetimes, which are used to obtain an estimate of the hazard function, which, in turn, is used to construct the survivor and density functions of the net lifetime of the variable of interest. If only one of the crude lifetimes can be adequately characterized by a parametric model, then semi-parametric estimates may be obtained using a maximum likelihood estimate of one crude lifetime and the empirical distribution function of the other. Simulation studies show that the survivor function estimates from crude lifetimes compare favourably with those given by the product-limit estimator when crude lifetimes are chosen correctly. Other advantages are discussed.     

Assessing goodness-of-fit of categorical regression models based on case-control data

Demonstrated equivalence between a categorical regression model based on case‐control data and an I‐sample semiparametric selection bias model leads to a new goodness‐of‐fit test. The proposed test statistic is an extension of an existing Kolmogorov–Smirnov‐type statistic and is the weighted average of the absolute differences between two estimated distribution functions in each response category. The paper establishes an optimal property for the maximum semiparametric likelihood estimator of the parameters in the I‐sample semiparametric selection bias model. It also presents a bootstrap procedure, some simulation results and an analysis of two real datasets.