用计算机网络辅助自主阅读提高 英语阅读能力

阅读教学是大学英语教学中的重中之重。当前日渐兴起的计算机网络辅助英语教学为提高学 生的阅读能力提供了更有利的环境。文章分析了计算机网络辅助自主阅读教学的优劣势,论 述了其可行性,并在此基础上提出了一些计算机网络辅助英语自主阅读的实施原则。     

计算机仿真、关系实在、场与多元论

文章从对计算机仿真技术的哲学思考出发 ,从一个角度对量子论到场论再到系统论加以论证 ,得出用关系实在取代实体的思路的合理性 ;认为海德格尔“世界”理论中就包含对从自然科学的场论到哲学场论在基本哲学层面的一种极好开拓方向 ,并以此作为多元论哲学的一种可行的基础 ,对当今思想界力图为多元论的存在进行合理性论证的历史主义和相对主义两种思潮中存在的问题进行了述评。     

Discovery,dissemination, and information diversity in networked groups

This paper examines the performance of networked groups on problems that require the aggregation of information from diverse sources. We present results from a behavioral experiment where groups of twenty networked participants worked on a problem that required them to pool unique information to discover a solution, and then disseminate it to others for adoption. We found that networks comprised of subgroups containing diverse information, which provided many opportunities for local synthesis of information, outperformed those comprised of homogeneous subgroups. Our findings suggest ways the configuration of a group can be altered to improve performance on a task that requires the coordinated discovery and implementation of a solution, illustrating that it is not only the total amount of information diversity in the group that matters but also its distribution within network subgroups.     

Fast regression surrogates for computer models with time-dependent outputs

The study of physical processes is often aided by computer models or codes. Computer models that simulate such processes are sometimes computationally intensive and therefore not very efficient exploratory tools. In this paper, we address computer models characterized by temporal dynamics and propose new statistical correlation structures aimed at modelling their time dependence. These correlations are embedded in regression models with input-dependent design matrix and input-correlated errors that act as fast statistical surrogates for the computationally intensive dynamical codes. The methods are illustrated with an automotive industry application involving a road load data acquisition computer model.     

A monte carlo study of the wald,lm, and lr tests in a heteroscedastic linear model

In this paper, we examine by Monte Carlo experiments the small sample properties of the W (Wald), LM (Lagrange Multiplier) and LR (Likelihood Ratio) tests for equality between sets of coefficients in two linear regressions under heteroscedasticity. The small sample properties of the size-corrected W, LM and LR tests proposed by Rothenberg (1984) are also examined and it is shown that the performances of the size-corrected W and LM tests are very good. Further, we examine the two-stage test which consists of a test for homoscedasticity followed by the Chow (1960) test if homoscedasticity is indicated or one of the W, LM or LR tests if heteroscedasticity should be assumed. It is shown that the pretest does not reduce much the bias in the size when the sizecorrected citical values are used in the W, LM and LR tests.     

Testing hypotheses in truncated samples by means of divergence statistics

In this paper we consider the problem of testing hypotheses in parametric models, when only the first r (of n) ordered observations are known.Using divergence measures, a procedure to test statistical hypotheses is proposed, Replacing the parameters by suitable estimators in the expresion of the divergence measure, the test statistics are obtained.Asymptotic distributions for these statistics are given in several cases when maximum likelihood estimators for truncated samples are considered.Applications of these results in testing statistical hypotheses, on the basis of truncated data, are presented.The small sample behavior of the proposed test statistics is analyzed in particular cases.A comparative study of power values is carried out by computer simulation.     

Some practical advice on polynomial regression analysis from blocked response surface designs

It is often necessary to run response surface designs in blocks. In this paper the analysis of data from such experiments, using polynomial regression models, is discussed. The definition and estimation of pure error in blocked designs are considered. It is recommended that pure error is estimated by assuming additive block and treatment effects, as this is more consistent with designs without blocking. The recovery of inter-block information using REML analysis is discussed, although it is shown that it has very little impact if the design is nearly orthogonally blocked. Finally prediction from blocked designs is considered and it is shown that prediction of many quantities of interest is much simpler than prediction of the response itself.     

Estimating standard error of intra-class correlation coefficients up to three level unbalanced nested clinical trials

ABSTRACT

Very often researchers plan a balanced design for cluster randomization clinical trials in conducting medical research, but unavoidable circumstances lead to unbalanced data. By adopting three or more levels of nested designs, they usually ignore the higher level of nesting and consider only two levels, this situation leads to underestimation of variance at higher levels. While calculating the sample size for three-level nested designs, in order to achieve desired power, intra-class correlation coefficients (ICCs) at individual level as well as higher levels need to be considered and must be provided along with respective standard errors. In the present paper, the standard errors of analysis of variance (ANOVA) estimates of ICCs for three-level unbalanced nested design are derived. To conquer the strong appeal of distributional assumptions, balanced design, equality of variances between clusters and large sample, general expressions for standard errors of ICCs which can be deployed in unbalanced cluster randomization trials are postulated. The expressions are evaluated on real data as well as highly unbalanced simulated data.     

Humerical. Results concerning the mle of an exponential parameter used in life testing

It was previously shown that the maximum likelihood estimator 0 of the scale parameter of the exponential distribution is asymptotically normal for type-I censoring. Applicability of the asymptotic normality results for finite samples is studied here by computer simulation for several different normalizing factors and for various levels of censoring. The use of the asymptotic results in statistical problems is illustrated by an example