“南斯拉夫模式”处理民族主义问题的失误

"南斯拉夫模式"作为社会主义国家处理民族问题的3个代表性模式之一,在处理民族主义问题的理论与实践方面存在诸多失误,主要是在"塞弱则南强"这一貌似正确的反对大民族主义策略下,未能充分认识地方民族主义对民族团结和联邦体制的破坏力,实际上助推了各加盟共和国地方民族主义的滋长,背离了马克思主义民族理论尤其是马克思主义经典作家关于正确认识和处理"两种民族主义"的相关论述,成为联盟解体和模式失败的重要因素。民族主义作为一种思潮,"沸点"较低,容易触发,在社会转型的时代,对"南斯拉夫模式"处理民族主义问题失误的反思,至今仍具有重要的理论和现实意义。     

论“兴”义的演变及其思维特点

既往对兴的理解大致可分为内容关联说、声韵限制说和音义关联说,但在实际文本中,却存在一种既无关内容,亦无关声韵的兴体。现有兴体多样化成因论多停留在事实的层面,着眼于历史语境等外在性因素,而缺乏对兴体自身的考察。因此,借鉴索绪尔的语言学理论,从自由的维度出发,我们发现作为表现手法的"兴"亦是人自由创造的产物,即是一种基于自由意志的音、义任意关联,它不是一种实体的指涉,而是一种关系的表述,这种关系,存在于兴句与其所对应的正句之间。     

国际合作中的集体行动问题

集体行动问题是指集体行动中因为个体的理性选择而导致集体选择的结果无法达到集体(或社会)效用最优,主要表现为"囚徒困境"、"集体行动的逻辑"和"阿罗悖论"三个定式.国际交往中的集体行动问题源于两个因素:不确定性和国际政治市场失灵,它们严重制约着国际合作的发生和持续.建立国际制度有助于克服集体行动问题,促进国际合作的顺利进行.     

联合竞争窗口和发送调整策略优化WLAN性能

IEEE802.11标准中所采用的二进制指数退避机制不能够根据网络当前的碰撞情况有效地使用无线资源,该文提出一种联合竞争窗口和发送时间调整策略,其中自适应竞争窗口方法以被动的方式根据网络当前数据帧碰撞程度快速调整竞争窗口,动态数据包发送时间调整机制使节点能够主动地调节单位时间内注入队列的数据包数量。仿真结果表明,该调整机制能够根据网络状态快速地、自适应地优化节点数据包传输过程中的相关参数,提高无线资源使用率,降低数据包时延。     

行业协会失灵的原因解读——从公益与私益冲突的角度

转型时期中国行业协会失灵的直接原因就在于其自身定位的公私冲突,其根源则在于政府和民众过分追求政治意义上的公益高于私益,忽视了行业协会作为市场经济的产物本身所固有的正当的私益追求。作为政府体制改革和经济体制改革的产物,行业协会定义的或公或私是民营化、市场化、非营利化三者之间博弈的结果,不具有任何褒贬意义。因此,继续纠缠于公益高于私益的行业体制改革已经走上了歧途。     

市场失灵与农地非农化配置中农地价值损失

目前,中国正处于经济快速增长、城市化进程加速、生态环境重建和农业结构新一轮调整的新阶段,农地资源面临着在农业和非农业之间以及两大部门内部不同产业之间配置的更大竞争力。但由于市场失灵的存在,农地的非市场价值在配置时往往被忽略掉,从而导致农地非农化配置效率低下。本文试图从市场失灵的内涵出发,深入分析市场失灵与农地非农化配置过程中农地价值损失的内在机理,从而为我国农地非农化的行为调控、实现机制及相应公共政策措施的制订和实施提供理论依据。     

A Non‐Parametric Estimator of the Spectral Density of a Continuous‐Time Gaussian Process Observed at Random Times

Abstract. In numerous applications data are observed at random times and an estimated graph of the spectral density may be relevant for characterizing and explaining phenomena. By using a wavelet analysis, one derives a non‐parametric estimator of the spectral density of a Gaussian process with stationary increments (or a stationary Gaussian process) from the observation of one path at random discrete times. For every positive frequency, this estimator is proved to satisfy a central limit theorem with a convergence rate depending on the roughness of the process and the moment of random durations between successive observations. In the case of stationary Gaussian processes, one can compare this estimator with estimators based on the empirical periodogram. Both estimators reach the same optimal rate of convergence, but the estimator based on wavelet analysis converges for a different class of random times. Simulation examples and an application to biological data are also provided.     

On Variance Components in Semiparametric Mixed Models for Longitudinal Data

Abstract. First, to test the existence of random effects in semiparametric mixed models (SMMs) under only moment conditions on random effects and errors, we propose a very simple and easily implemented non‐parametric test based on a difference between two estimators of the error variance. One test is consistent only under the null and the other can be so under both the null and alternatives. Instead of erroneously solving the non‐standard two‐sided testing problem, as in most papers in the literature, we solve it correctly and prove that the asymptotic distribution of our test statistic is standard normal. This avoids Monte Carlo approximations to obtain p ‐values, as is needed for many existing methods, and the test can detect local alternatives approaching the null at rates up to root n. Second, as the higher moments of the error are necessarily estimated because the standardizing constant involves these quantities, we propose a general method to conveniently estimate any moments of the error. Finally, a simulation study and a real data analysis are conducted to investigate the properties of our procedures.     

Variability explained by covariates in linear mixed‐effect models for longitudinal data

Variability explained by covariates or explained variance is a well‐known concept in assessing the importance of covariates for dependent outcomes. In this paper we study R² statistics of explained variance pertinent to longitudinal data under linear mixed‐effect models, where the R² statistics are computed at two different levels to measure, respectively, within‐ and between‐subject variabilities explained by the covariates. By deriving the limits of R² statistics, we find that the interpretation of explained variance for the existing R² statistics is clear only in the case where the covariance matrix of the outcome vector is compound symmetric. Two new R² statistics are proposed to address the effect of time‐dependent covariate means. In the general case where the outcome covariance matrix is not compound symmetric, we introduce the concept of compound symmetry projection and use it to define level‐one and level‐two R² statistics. Numerical results are provided to support the theoretical findings and demonstrate the performance of the R² statistics. The Canadian Journal of Statistics 38: 352–368; 2010 © 2010 Statistical Society of Canada     

Asymptotic Properties for a Class of Partially Identified Models

We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV). An SVRV is a mapping that associates a set (rather than a real number) with each element of the sample space. Examples of population features in this class include interval‐identified scalar parameters, best linear predictors with interval outcome data, and parameters of semiparametric binary models with interval regressor data. We extend the analogy principle to SVRVs and show that the sample analog estimator of the population identification region is given by a transformation of a Minkowski average of SVRVs. Using the results of the mathematics literature on SVRVs, we show that this estimator converges in probability to the population identification region with respect to the Hausdorff distance. We then show that the Hausdorff distance and the directed Hausdorff distance between the population identification region and the estimator, when properly normalized by , converge in distribution to functions of a Gaussian process whose covariance kernel depends on parameters of the population identification region. We provide consistent bootstrap procedures to approximate these limiting distributions. Using similar arguments as those applied for vector valued random variables, we develop a methodology to test assumptions about the true identification region and its subsets. We show that these results can be used to construct a confidence collection and a directed confidence collection. Those are (respectively) collection of sets that, when specified as a null hypothesis for the true value (a subset of values) of the population identification region, cannot be rejected by our tests.