民办幼儿园教师专业素养培训方式实验研究

随着现代教育的发展，提升幼儿园教师专业素养已成为促进学前教育发展的必然要求。以探究民办幼儿园教师专业素养为视角，提出通过干预行为提升教师专业素养水平的构想。实验随机选取成都市某民办幼儿园的20名教师为实验组，再选取与其办园环境、办园背景相似的另一所民办幼儿园的20名教师为对照组，对实验园教师的专业理念与师德、专业知识、专业能力3个维度进行干预。实验结果表明：与对照园相比，实验园教师的专业能力提升显著；与实验园实验前后相比，教师在专业知识、专业能力维度及专业素养总体水平方面均有提升。对教师培训的具体建议为：（1）单一与混合相结合的培训方式对提升民办幼儿园教师专业素养总体水平有现实意义；（2）民办幼儿园教师专业知识的提升需理论培训与自主学习相结合；（3）专家"现场诊断-指导式"培训方式对提升民办幼儿园教师专业能力有显著作用。综上，建议民办幼儿园在了解本园教师专业素养水平的基础上，针对教师专业素养的不同方面，分别采取不同的有效培训方式。     

内核级并发通信的研究

提出了一种内核级并发消息通信机制。该机制采用对象传送协议和动态线程池技术,并通过会话控制完成数据的收发过程,将线程池设计为二级阻塞队列来暂缓线程的撤销过程,从而保证既能够快速响应客户请求,又可减少因频繁创建和撤销线程而消耗系统的资源和时间,为分布式操作系统提供了高效可靠的通信服务。     

Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches

Summary.  Alongside the development of meta-analysis as a tool for summarizing research literature, there is renewed interest in broader forms of quantitative synthesis that are aimed at combining evidence from different study designs or evidence on multiple parameters. These have been proposed under various headings: the confidence profile method, cross-design synthesis, hierarchical models and generalized evidence synthesis. Models that are used in health technology assessment are also referred to as representing a synthesis of evidence in a mathematical structure. Here we review alternative approaches to statistical evidence synthesis, and their implications for epidemiology and medical decision-making. The methods include hierarchical models, models informed by evidence on different functions of several parameters and models incorporating both of these features. The need to check for consistency of evidence when using these powerful methods is emphasized. We develop a rationale for evidence synthesis that is based on Bayesian decision modelling and expected value of information theory, which stresses not only the need for a lack of bias in estimates of treatment effects but also a lack of bias in assessments of uncertainty. The increasing reliance of governmental bodies like the UK National Institute for Clinical Excellence on complex evidence synthesis in decision modelling is discussed.     

Using local linear kernel smoothers to test the lack of fit of nonlinear regression models

Herein, we propose a data-driven test that assesses the lack of fit of nonlinear regression models. The comparison of local linear kernel and parametric fits is the basis of this test, and specific boundary-corrected kernels are not needed at the boundary when local linear fitting is used. Under the parametric null model, the asymptotically optimal bandwidth can be used for bandwidth selection. This selection method leads to the data-driven test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The finite-sample property of the proposed data-driven test is illustrated, and the power of the test is compared with that of some existing tests via simulation studies. We illustrate the practicality of the proposed test by using two data sets.     

Kernel Estimation of Rate Function for Recurrent Event Data

Abstract.  Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth will produce a curve with nicks occurring at the censoring times, whereas there is no such problem with the least squares method. Furthermore, the asymptotic variance of the least squares estimator is shown to be smaller under regularity conditions. However, in the implementation of the bootstrap procedures, the moment method is computationally more efficient than the least squares method because the former approach uses condensed bootstrap data. The performance of the proposed procedures is studied through Monte Carlo simulations and an epidemiological example on intravenous drug users.     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.     

立足高起点发展新方向培养应用型复合型人才——对我院法学专业教学的认识及建议

我院法学专业培养目标是为社会培养应用型、复合型人才 ,针对实际情况 ,作者提出应改革教学内容 ,优化课程体系 ;改革教学方法 ,完善教学手段 ;提高教师业务水平 ,加强学术梯队建设等建议。     

A Semi-parametric Regression Model with Errors in Variables

Abstract.  In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non-parametric kernel estimator. Strong convergence, optimal rate of weak convergence and asymptotic normality of the estimators are investigated.     

刘熙载楚辞学的研究方法

刘熙载在研究楚辞时,运用了以意逆志法、多重比较法、举借概括法等方法,从而形成了一套颇具特色的文学批评法。     

约束优化问题的异步并行拟牛顿方法

提出了一种求解等式约束优化问题的异步并行拟牛顿方法 .若假设目标函数 f和约束函数h至少三次连续可微 ,且△h(x)对任意x∈Rn 均为满秩矩阵 ,证明了所提出的异步并行算法是 q—超线性收敛的 .