六端口技术自校准方法解的不唯一性证明

本文就六端口技术自校准方法进行了分析讨论,用解析方法和计算机模拟方法分别证明了自校准方法解的不唯一性.从而否定了自校准方法是六端口技术最突出的优点之一这个传统概念.     

人机工程学的发展及人机系统的可靠性

本文介绍了人机工程学(工效学)这一新兴边缘学科的研究内容、发展及现状,讨论了人-机系统的可靠性并着重讨论了人的操作可靠性,探讨了按人为差错进行可靠性分析与可靠性估计的人为差错和可靠性分析逻辑推演法(HERALD法)及系统能力方法(SC方法)。 本文的结论可供系统管理工作者及设计工作者,特别是可靠性管理及可靠性设计工作者参考。     

聚焦中国英语课堂:影响学生参与讨论的因素—— 一次采用定性分析法进行本土化研究的尝试

通过课堂观察及课后的学生采访,本文采用定性研究法考察了决定英语课堂上中国学生讨论情况的因素。　　本文的主要发现是：（１） 中国文化推崇的谦虚美德明显地妨碍了学生们抒发己见;（２） 学生推理能力和英语表达能力之间的鸿沟是引起表达焦虑的主要原因;（３） 教师的角色左右学生讨论中表现。基于上述发现,本文认为,课堂学习由民族性格、教育体制、口语习得心理以及教师角色所决定。因此建议,今后要注意研究课堂学习和其他影响因素之间的关系     

计算机病毒及其防治的探讨

本文阐述了计算机病毒的概念,对计算机病毒进行了分类,同时分析了各类病毒的特点。并着重介绍了对病毒的防范措施以及清除的方法。     

广东普通工科高等教育的办学效益分析研究

对广东普通工科高等教育的办学效益问题,从结构布局效益、层次与规模效益进行了讨论,提出了提高办学效益的基本对策。     

中学课外科技活动的存在问题及对策

本文较为详细地分析我们在１９９８ 年上半年进行的“广州 市中学生课外科技活动问卷调查”的数据, 指出广州市中学生课外科技 活动存在着师资力量不足、考核制度不完善等问题, 文章最后提出了解 决上述问题的意见和看法     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

The role of tobacco taxes in starting and quitting smoking: Duration analysis of British data

The annual 5% increase in tobacco taxes in real terms proposed in the recent White Paper on smoking has reaffirmed the commitment of successive UK Governments to above-inflation increases in tobacco taxation to encourage people to stop smoking. This paper presents evidence on the determinants of starting and quitting smoking by using data from the British Health and Lifestyle Survey and is the first to identify tax elasticities for starting and quitting smoking using British data. Self-reported individual smoking histories are coupled with a long time series for the tax rate on cigarettes to construct a longitudinal data set. Estimates are obtained for the effect of above-inflation tax rises on the age of starting smoking and the number of years of smoking. The estimates of the tax elasticity of the age of starting smoking are 0.16 for men and 0.08 for women. The estimates of the tax elasticity of quitting are −0.60 for men and −0.46 for women. These are robust to different specifications.     

Bayesian inference with misspecified models

This article reviews Bayesian inference from the perspective that the designated model is misspecified. This misspecification has implications in interpretation of objects, such as the prior distribution, which has been the cause of recent questioning of the appropriateness of Bayesian inference in this scenario. The main focus of this article is to establish the suitability of applying the Bayes update to a misspecified model, and relies on representation theorems for sequences of symmetric distributions; the identification of parameter values of interest; and the construction of sequences of distributions which act as the guesses as to where the next observation is coming from. A conclusion is that a clear identification of the fundamental starting point for the Bayesian is described.     

Parameter estimation of nonlinear mixed-effects models using first-order conditional linearization and the EM algorithm

Nonlinear mixed-effects (NLME) models are flexible enough to handle repeated-measures data from various disciplines. In this article, we propose both maximum-likelihood and restricted maximum-likelihood estimations of NLME models using first-order conditional expansion (FOCE) and the expectation–maximization (EM) algorithm. The FOCE-EM algorithm implemented in the ForStat procedure SNLME is compared with the Lindstrom and Bates (LB) algorithm implemented in both the SAS macro NLINMIX and the S-Plus/R function nlme in terms of computational efficiency and statistical properties. Two realworld data sets an orange tree data set and a Chinese fir (Cunninghamia lanceolata) data set, and a simulated data set were used for evaluation. FOCE-EM converged for all mixed models derived from the base model in the two realworld cases, while LB did not, especially for the models in which random effects are simultaneously considered in several parameters to account for between-subject variation. However, both algorithms had identical estimated parameters and fit statistics for the converged models. We therefore recommend using FOCE-EM in NLME models, particularly when convergence is a concern in model selection.