SoC软硬件协同技术的FPGA芯片测试新方法

针对传统的基于纯硬件平台的FPGA芯片测试方法所存在的种种问题,提出并验证了一种基于软硬件协同技术的FPGA芯片测试方法。该方法引入了软件的灵活性与可观测性等软件技术优势,具有存储深度大、可测I/O管脚数目多、自动完成配置下载(不需人工干预)和自动定位FPGA中的错误等优点,提高了FPGA的测试速度和可靠性,并降低了测试成本,与传统的自动测试仪(ATE)相比有较高的性价比。采用软硬件协同方式针对Xilinx4010的I/O单元进行了测试,实现了对FPGA芯片的自动反复配置、测试和错误定位。     

Nonparametric Tests for Stratum Effects in the Cox Model

Thispaper considers the stratified proportional hazards model witha focus on the assessment of stratum effects. The assessmentof such effects is often of interest, for example, in clinicaltrials. In this case, two relevant tests are the test of stratuminteraction with covariates and the test of stratum interactionwith baseline hazard functions. For the test of stratum interactionwith covariates, one can use the partial likelihood method (Kalbfleischand Prentice, 1980; Lin, 1994). For the test of stratum interactionwith baseline hazard functions, however, there seems to be noformal test available. We consider this problem and propose aclass of nonparametric tests. The asymptotic distributions ofthe tests are derived using the martingale theory. The proposedtests can also be used for survival comparisons which need tobe adjusted for covariate effects. The method is illustratedwith data from a lung cancer clinical trial.     

Hypothesis Testing of Hazard Ratio Parameters in Marginal Models for Multivariate Failure Time Data

Marginal hazard models for multivariate failure time data have been studied extensively in recent literature. However, standard hypothesis test statistics based on the likelihood method are not exactly appropriate for this kind of model. In this paper, extensions of the three commonly used likelihood hypothesis test statistics are discussed. Generalized Wald, generalized score and generalized likelihood ratio tests for hazard ratio parameters in a marginal hazard model for multivariate failure time data are proposed and their asymptotic distributions examined. The finite sample properties of these statistics are studied through simulations. The proposed method is applied to data from Busselton Population Health Surveys.     

Assessing Effects on Long-term Survival after Early Termination of Randomized Trials

In this paper we present an approach to using historical control data to augment information from a randomized controlled clinical trial, when it is not possible to continue the control regimen to obtain the most reliable and valid assessment of long term treatment effects. Using an adjustment procedure to the historical control data, we investigate a method of estimating the long term survival function for the clinical trial control group and for evaluating the long term treatment effect. The suggested method is simple to interpret, and particularly motivated in clinical trial settings when ethical considerations preclude the long term follow-up of placebo controls. A simulation study reveals that the bias in parameter estimates that arises in the setting of group sequential monitoring will be attenuated when long term historical control information is used in the proposed manner. Data from the first and second National Wilms' Tumor studies are used to illustrate the method.     

Rank Tests for Matched Survival Data

In a clinical trial with the time to an event as the outcome of interest, we may randomize a number of matched subjects, such as litters, to different treatments. The number of treatments equals the number of subjects per litter, two in the case of twins. In this case, the survival times of matched subjects could be dependent. Although the standard rank tests, such as the logrank and Wilcoxon tests, for independent samples may be used to test the equality of marginal survival distributions, their standard error should be modified to accommodate the possible dependence of survival times between matched subjects. In this paper we propose a method of calculating the standard error of the rank tests for paired two-sample survival data. The method is naturally extended to that for K-sample tests under dependence.     

The information matrix test in the linear regression with ARMA errors

Summary The paper shows that the informaton matrix test presented by White (1982) decomposes into the sum of quadratic forms in the case of a linear model with ARMA errors. By extending previous results, which analysed the information matrix test in the presence of serial correlation, the test allows detection of additional sources of misspecification.     

AN ANALYTIC FORM FOR THE CUMULATIVE DISTRIBUTION FUNCTION OF THE BELL-DOKSUM STATISTIC

A closed form analytic expression for the Bell-Doksum Statistic is developed. The use of the generalized hypergeometric function to evaluate the expression is demonstrated, and a table of critical values for 2 n 25 is presented.     

Merging data from multiple sources: pretest and shrinkage perspectives

In this article, we have developed asymptotic theory for the simultaneous estimation of the k means of arbitrary populations under the common mean hypothesis and further assuming that corresponding population variances are unknown and unequal. The unrestricted estimator, the Graybill-Deal-type restricted estimator, the preliminary test, and the Stein-type shrinkage estimators are suggested. A large sample test statistic is also proposed as a pretest for testing the common mean hypothesis. Under the sequence of local alternatives and squared error loss, we have compared the asymptotic properties of the estimators by means of asymptotic distributional quadratic bias and risk. Comprehensive Monte-Carlo simulation experiments were conducted to study the relative risk performance of the estimators with reference to the unrestricted estimator in finite samples. Two real-data examples are also furnished to illustrate the application of the suggested estimation strategies.     

中国产业结构变动与就业水平的实证研究

文章运用时间序列和计量模型研究了中国产业结构与就业水平之间的发展关系.首先运用格兰杰因果检验分析了第一、二、三次产业的产值与其就业之间的因果影响关系,继而从产值与就业两个角度定量分析了第一、二、三次产业各自对总就业水平的贡献,得出结论:第一产业和第三产业是劳动力就业的决定因素,而第二产业不是就业的决定因素;从产值角度来看,第三产业对劳动力就业的贡献最大,从就业角度来看,第一产业的就业对总体就业水平的贡献最高,其次是第三产业,而第二产业的就业对总体就业水平的贡献最小.最后根据研究结论提出相应的建议措施.