蓝宝石微带色散特性研究

本文将谱域导纳法推广应用于各向异性介质基片微带,通过详细的理论分析,导出了各向异性等效传输线的特性导纳公式,并考虑了导带厚度的影响,建立了各向异性介质基片平面传输线传播常数色散特性的计算机程序。用本文推导的结果进行数值计算,在2-18GHz内求得的蓝宝石微带色散曲线与文献[1]的结果吻合得很好。此外,本方法直接、简明、且计算量小、适用于各类微波平面传输线的分析。因而,其具有较大的工程实用价值。     

分层双各向异性媒质中基本振子源的电磁场(英文)

对于观察点不在任意方向放置的,位于分层双各向异性媒质中的基本电磁振子源附近的电磁场,本文给出了明显表达式。利用傅立叶变换谱域中的横向场矢量的矩阵分析,该问题得到了较为方筻的解决。     

近10年来我国农民工问题研究述评

农民工问题是中国农民在城镇化、现代化进程中伴生的重大人口与经济社会问题。以《中国青年研究》近10年来199篇刊文为样本,分析发现,农民工问题研究主要集中在民生保障、流动融入、家庭生活、政治维权及随迁子女教育等几大方面。研究者问题意识强烈,论述逻辑清晰;研究视角多元纷呈,理论阐释有待提升;注重实证分析但操作方法尚需科学规范,研究论域值得进一步拓展和深化,特别是女性农民工、流动儿童、中老年农民工及其他相关弱势群体需要重点关注。     

我国高校教师教学能力研究的问题域、反思及其走向

我国高校教师教学能力研究的问题域主要体现为:认识论研究,教学能力是什么;价值论研究,教学能力的作用;方法论研究,教学能力如何提升。研究中存在的问题是:研究视角偏窄,研究主题覆盖面小;研究方法单一,研究结论针对性不强;宏观规划研究缺乏,制度体系研究不够。今后的研究中要考虑教师和学生的主体地位和需求,强化量化研究与质性研究的协同,注重高校之间的横向联系,加强规章制度建设。     

Frequency domain bootstrap for ratio statistics under long-range dependence

A frequency domain bootstrap (FDB) is a common technique to apply Efron’s independent and identically distributed resampling technique (Efron, 1979) to periodogram ordinates – especially normalized periodogram ordinates – by using spectral density estimates. The FDB method is applicable to several classes of statistics, such as estimators of the normalized spectral mean, the autocorrelation (but not autocovariance), the normalized spectral density function, and Whittle parameters. While this FDB method has been extensively studied with respect to short-range dependent time processes, there is a dearth of research on its use with long-range dependent time processes. Therefore, we propose an FDB methodology for ratio statistics under long-range dependence, using semi- and nonparametric spectral density estimates as a normalizing factor. It is shown that the FDB approximation allows for valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without any stringent assumptions on the distribution of the underlying process. The results of a large simulation study show that the FDB approximation using a semi- or nonparametric spectral density estimator is often robust for various values of a long-memory parameter reflecting magnitude of dependence. We apply the proposed procedure to two data examples.     

Asymptotic Distributions of Quasi‐Maximum Likelihood Estimators for Spatial Autoregressive Models

This paper investigates asymptotic properties of the maximum likelihood estimator and the quasi‐maximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is important to make the distinction with different spatial scenarios. Under the scenario that each unit will be influenced by only a few neighboring units, the estimators may have ‐rate of convergence and be asymptotically normal. When each unit can be influenced by many neighbors, irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence.     

A Bias–Reduced Log–Periodogram Regression Estimator for the Long–Memory Parameter

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^d_r, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^d_r, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^d_r. These results show that the bias of ^d_r goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^d_r attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.     

网络环境版权相关法律问题

版权保护制度受技术发展的影响极大。数字传播技术和因特网的广泛使用,打破了版权法的利益平衡,对版权法的主要概念以及版权人的相关经济权利造成了一定的影响。在网络环境中,作品具有更大的开放性,因而具有极强的、难以克服的外部经济效果,版权人排除他人对作品进行使用的成本极高。在网络环境中,网络作品的私有将变得非常困难,网络环境中版权制度的走向是,版权将变为一种公共权利,作品由政府提供将是惟一的选择。     

Estimate for the Finite-time Ruin Probability in the Discrete-time Risk Model with Insurance and Financial Risks

In this article, we consider a discrete-time risk model with insurance and financial risks. We derive some refinements of a general asymptotic formula for the finite-time ruin probability under the assumptions that the net losses follow a common distribution in the intersection between the subexponential class and the Gumbel maximum domain of attraction, and the stochastic discount factors of the risky asset have a common distribution with extended regular variation. The obtained asymptotic upper and lower bounds are transparent and computable.     

Some Theoretical and Simulation Results on the Frequency Domain Causality Test

Breitung and Candelon (2006 Breitung , J. , Candelon , B. ( 2006 ). Testing for short- and long-run causality: A frequency-domain approach . Journal of Econometrics 132 : 363 – 378 .[Crossref], [Web of Science ®] , [Google Scholar]) in Journal of Econometrics proposed a simple statistical testing procedure for the noncausality hypothesis at a given frequency. In their paper, however, they reported some theoretical results indicating that their test severely suffers from quite low power when the noncausality hypothesis is tested at a frequency close to 0 or pi. This paper examines whether or not these results indicate their procedure is useless at such frequencies.