On the Optimal Use of Suboptimal Forecasts of Explanatory Variables

Ashley (1983) gave a simple condition for determining when a forecast of an explanatory variable (X_t ) is sufficiently inaccurate that direct replacement of X_t by the forecast yields worse forecasts of the dependent variable than does respecification of the equation to omit X_t . Many available macroeconomic forecasts were shown to be of limited usefulness in direct replacement. Direct replacement, however, is not optimal if the forecast's distribution is known. Here optimal linear forms in commercial forecasts of several macroeconomic variables are obtained by using estimates of their distributions. Although they are an improvement on the raw forecasts (direct replacement), these optimal forms are still too inaccurate to be useful in replacing the actual explanatory variables in forecasting models. The results strongly indicate that optimal forms involving several commercial forecasts will not be very useful either. Thus Ashley's (1983) sufficient condition retains its value in gauging the usefulness of a forecast of an explanatory variable in a forecasting model, even though it focuses on direct replacement.     

齿轮精度的计算机辅助设计

介绍用VisualBasic语言编写的软件来完成齿轮精度设计工作。该软件操作简单、能迅速正确地确定齿轮三个公差组的精度等级、齿厚极限偏差代号、各公差组的检验项目及其公差值。     

略论法律语言的模糊性

法律语言最重要的特点就是准确性。而实际上,为了使得法律语言更具准确性,法律语言在很多情况下也要借助于模糊语言的使用。结合实例,探讨了法律语言中模糊语言使用的原因以及语用功能。     

Bayesian sample size determination for estimating binomial parameters from data subject to misclassification

We investigate the sample size problem when a binomial parameter is to be estimated, but some degree of misclassification is possible. The problem is especially challenging when the degree to which misclassification occurs is not exactly known. Motivated by a Canadian survey of the prevalence of toxoplasmosis infection in pregnant women, we examine the situation where it is desired that a marginal posterior credible interval for the prevalence of width w has coverage 1−α, using a Bayesian sample size criterion. The degree to which the misclassification probabilities are known a priori can have a very large effect on sample size requirements, and in some cases achieving a coverage of 1−α is impossible, even with an infinite sample size. Therefore, investigators must carefully evaluate the degree to which misclassification can occur when estimating sample size requirements.     

知识产权法定赔偿面临的问题及对策

知识产权法定赔偿存在定性不清、适用过滥的问题，由于对酌定情节在把握尺度上存在差异，法定赔偿缺乏应有的精确性，在相当程度上扭曲了知识产权法定赔偿的立法本意。解决知识产权法定赔偿面临的问题，必须坚持非惩罚性原则，不仅权利人应当就侵权损失承担举证责任，而且客观上应当尽量缩小法定赔偿的酌定范围，以增强法定赔偿额的可预测性和司法统一性。     

Bayesian Interval Estimation for the Difference in TPRs and FPRs of Two Diagnostic Tests with Unverified Negatives

We derive Bayesian interval estimators for the differences in the true positive rates and false positive rates of two dichotomous diagnostic tests applied to the members of two distinct populations. The populations have varying disease prevalences with unverified negatives. We compare the performance of the Bayesian credible interval to the Wald interval using Monte Carlo simulation for a spectrum of different TPRs, FPRs, and sample sizes. For the case of a low TPR and low FPR, we found that a Bayesian credible interval with relatively noninformative priors performed well. We obtain similar interval comparison results for the cases of a high TPR and high FPR, a high TPR and low FPR, and of a high TPR and mixed FPR after incorporating mildly informative priors.     

关于如何完善本科成绩核查系统的探讨

目前本科成绩核查系统存在很多缺陷,要不断在各个方面进行进一步完善,避免错登、漏登等情况发生,提高成绩核查的准确性.     

On a mixture vector autoregressive model

The authors show how to extend univariate mixture autoregressive models to a multivariate time series context. Similar to the univariate case, the multivariate model consists of a mixture of stationary or nonstationary autoregressive components. The authors give the first and second order stationarity conditions for a multivariate case up to order 2. They also derive the second order stationarity condition for the univariate mixture model up to arbitrary order. They describe an EM algorithm for estimation, as well as a diagnostic checking procedure. They study the performance of their method via simulations and include a real application.     

Unraveling the Source of African American Children's Positively Biased Perceptions of Peer Acceptance

The present study investigated possible ethnic contributions to overly positive self‐perceptions in middle childhood. The goals of this study were threefold. First, the present study sought to replicate the intriguing findings reported by Zakriski and Coie that African American children overestimate their acceptance, and European American children underestimate acceptance by other‐ethnicity peers. Second, this study examined possible explanations for ethnic differences in the pattern of perceptual bias. Finally, this study extended prior research by examining ethnic differences in the accuracy of children's perceived peer acceptance. Archival data consisting of 826 children in third (N = 284), fourth (N = 241), and fifth grades (N = 301) were used in the present investigation; 237 of which were African American children, and 589 were European American children. Results of this study replicated the findings of Zakriski and Coie . Moreover, African Americans' overestimation and European Americans' underestimation of acceptance by other‐ethnicity peers was found to be attributable to more positive views of self and others among African American children relative to European American children. Finally, children were found to be more accurate about judging their acceptance by peers of the same ethnicity than those of a different ethnicity. Possible explanations of what causes African American children to have more positive views of self and others than European American children are discussed.     

On the relative performance of the block bootstrap for dependent data

In the independent setting, both Efron's bootstrap and “empiricai Edgeworth expansion” (E.E-expansion) give second-order accurate approximations to distributions of standardized and studentized statistics in the smooth function model. As a result, Efron's bootstrap was often regarded as roughly equivalent to the one-term E.E-expansion. However, a more detailed analysis shows that Efron's bootstrap outperforms the E.E-expansion in terms of loss functions by Bhattacharya and Qumsiyeh (1989) and in terms of probabilities for large deviations by Hall (1990) and Jing et a1 (1994). in this paper, we shall study the performances of the block bootstrap and the E.E-expansion for the weakly dependent data. It turns out that similar properties hold:both perform equally well at the center of the distribution but the block bootstrap provides accurate approximations even in the tails of the distributions. The study is focued on the simple case of standardized and studentized sample mean, but the conclusions can be easily extended to the smooth function of multivariate means.