英语文体学与大学英语精读教学

在大学英语精读教学中,适当运用文体学教法,可以培养学生学习英语的兴趣,有助于提高学生听说读写的能力,以便进行有效的交流,同时也有助于教学目标的顺利完成.     

On a test for structural stability of euler conditions parameters estimated via the generalized method of moments estimator: small sample properties

This paper presents Monte Carlo experiments on the small sample performance of the predictive test for structural change proposed by Ghysels and Hall. The predictive test was found to be more powerful than the overidentifying restrictions test in terms of size-corrected power when a shift in parameter has occurred. Also, it was found that the power of the predictive test decreases drastically as the number of the out-sample data decreases.     

对高校健美操选项课中培养学生创新能力的实验研究

高校体育选项课的开设 ,应以满足学生爱好、适合学生需求并能促进学生个性发展为目的。本实验以创造性教学理论为指导 ,根据学生个性发展的需要 ,把教学重点放在培养学生创新意识和创编能力上 ,设计并进行了“学生创编健美操教学模式”的研究。经过三学年共 4 2 8人的实验 ,本实验研究取得了良好的教学效果。 1998级、 1999级、 2 0 0 0级实验组学生的平均成绩均高于对照组 ,且呈显著性差异。研究结果表明 ,“学生创编健美操教学模式”更有助于提高学生学习健美操的能力和效率 ,对满足学生爱好、促进学生个性发展具有良好的作用 ,并为学生进行终身体育锻炼奠定了良好的基础。同时 ,无论是学生的学习兴趣、课堂气氛 ,还是掌握动作的速度及掌握动作的准确程度 ,实验组均优于对照组     

以数控技能大赛为平台提高数控技术师资的教学水平

针对现有数控技术师资队伍中存在教学水平参差不齐现象,通过参加全国数控技能大赛这种方式,在理论知识、软件应用、实际操作技能等方面,提高数控技术师资队伍的教学水平。为高等学校工程训练中心师资队伍教学水平的提升,开辟了一条有效的途径。     

电子实验中心的建设与探索

本文介绍了如何充分利用现有设备资源,进行电子实验中心的硬件建设,改革实验室管理制度,进行师资建设,培养具有工程素质和创新能力的学生。     

Inference from the Exponentiated Weibull Model with Applications to Real Data

In this paper, a novel Bayesian framework is used to derive the posterior density function, predictive density for a single future response, a bivariate future response, and several future responses from the exponentiated Weibull model (EWM). We study three related types of models, the exponentiated exponential, exponentiated Weibull, and beta generalized exponential, which are all utilized to determine the goodness of fit of two real data sets. The statistical analysis indicates that the EWM best fits both data sets. We determine the predictive means, standard deviations, highest predictive density intervals, and the shape characteristics for a single future response. We also consider a new parameterization method to determine the posterior kernel densities for the parameters. The summary results of the parameters are calculated by using the Markov chain Monte Carlo method.     

PMSE performance of two different types of preliminary test estimators under a multivariate t error term

Abstract

In this paper, assuming that the error terms follow a multivariate t distribution, we derive the exact formula for the predictive mean squared error (PMSE) of two different types of pretest estimators. It is shown analytically that one of the pretest estimator dominates the SR estimator if a critical value of the pretest is chosen appropriately. Also, we compare the PMSE of the pretest estimators with the MMSE, AMMSE, SR and PSR estimators by numerical evaluations. Our results show that the pretest estimators dominate the OLS estimator for all combinations when the degrees of freedom is not more than 5.     

Optimal control designs using predicting densities for the multivariate linear model

We provide an application of a variety of predicting densities to quality control involving multivariate normal linear models. We produce optimal control designs for single muleivaiiate future observations using predicting densities employing estimative, profile likelihood, Hinkley-Lauritzen, Butler, Bayesian, and Parametric Bootstrap methodologies. The decision-theoretic optimality criterion is an intuitively appealing quadratic consumer-producer risk function. The optimal control design arising from an optimal Kullback-Leibler frequentist prediction density is shown to coincide with that arising from an optimal Kullback-Leibler Bayesian predictive density. An example involving EVOP is provided to illustrate the methodology and to raise questions concerning the relative merics of the variety of predictive approaches in the quality control context.     

Comment

We propose a sequential test for predictive ability for recursively assessing whether some economic variables have explanatory content for another variable. In the forecasting literature it is common to assess predictive ability by using “one-shot” tests at each estimation period. We show that this practice leads to size distortions, selects overfitted models and provides spurious evidence of in-sample predictive ability, and may lower the forecast accuracy of the model selected by the test. The usefulness of the proposed test is shown in well-known empirical applications to the real-time predictive content of money for output and the selection between linear and nonlinear models.     

Prediction Techniques for Box–Cox Regression Models

This article reviews several techniques useful for forming point and interval predictions in regression models with Box-Cox transformed variables. The techniques reviewed—plug-in, mean squared error analysis, predictive likelihood, and stochastic simulation—take account of nonnormality and parameter uncertainty in varying degrees. A Monte Carlo study examining their small-sample accuracy indicates that uncertainty about the Box–Cox transformation parameter may be relatively unimportant. For certain parameters, deterministic point predictions are biased, and plug-in prediction intervals are also biased. Stochastic simulation, as usually carried out, leads to badly biased predictions. A modification of the usual approach renders stochastic simulation predictions largely unbiased.