论无形资本的扩张性

第三次经济全球化正风起云涌,在此背景下,无形资本的作用日显重要。本文从价格与价值的关系谈起,揭示了无形资本超额利润的来源,并得出结论,在超额利润的驱动下,资本家必然力图扩大无形资本的生产范围和规模,也就是说无形资本具有更为强烈的扩张趋向。     

Testing equality of mean vectors in a one-way MANOVA with monotone missing data

In this study, testing the equality of mean vectors in a one-way multivariate analysis of variance (MANOVA) is considered when each dataset has a monotone pattern of missing observations. The likelihood ratio test (LRT) statistic in a one-way MANOVA with monotone missing data is given. Furthermore, the modified test (MT) statistic based on likelihood ratio (LR) and the modified LRT (MLRT) statistic with monotone missing data are proposed using the decomposition of the LR and an asymptotic expansion for each decomposed LR. The accuracy of the approximation for the Chi-square distribution is investigated using a Monte Carlo simulation. Finally, an example is given to illustrate the methods.     

Large deviation expansion for maximum-likelihood estimator of α −Brownian bridge

We studied the large deviation expansion for maximum-likelihood estimator of α ?Brownian bridge by using change of measure and time-varying change of probability. The full expansion provided a useful tool to approximate the tail probability of maximum-likelihood estimator.     

Structural identification and variable selection in high-dimensional varying-coefficient models

Varying-coefficient models have been widely used to investigate the possible time-dependent effects of covariates when the response variable comes from normal distribution. Much progress has been made for inference and variable selection in the framework of such models. However, the identification of model structure, that is how to identify which covariates have time-varying effects and which have fixed effects, remains a challenging and unsolved problem especially when the dimension of covariates is much larger than the sample size. In this article, we consider the structural identification and variable selection problems in varying-coefficient models for high-dimensional data. Using a modified basis expansion approach and group variable selection methods, we propose a unified procedure to simultaneously identify the model structure, select important variables and estimate the coefficient curves. The unique feature of the proposed approach is that we do not have to specify the model structure in advance, therefore, it is more realistic and appropriate for real data analysis. Asymptotic properties of the proposed estimators have been derived under regular conditions. Furthermore, we evaluate the finite sample performance of the proposed methods with Monte Carlo simulation studies and a real data analysis.     

Correctings tests for trend in proportions for skewness

We develop the score test for the hypothesis that a parameter of a Markov sequence is constant over time, against the alternatives that it varies over time, i.e., θ_t = θ + U_t; t = 1,2,…, where {U_t; t = 1,2,...} is a sequence of independently and identically distributed random variables with mean zero and variance σ^z _u and θ is a fixed constant. The asymptotic null distribution of the test statistic is proved to be normal. We illustrate our procedure by examples and a real life data analysis.     

Harmonic mean approach to uunbalanced random effects models under heteroscedasticity

In this paper we consider unbalanced random effects models under heteroscedastic variances. By using' the harmonic mean approach, it is shown that the problems are analogous to those from balanced random effects models under horaoscedastic variances. Thus, by using the harmonic mean approach, statistical inferences about variance components are derived by using procedures from balanced models under homoscedastic variances. Laguerre polynomial expansion is used to approximate the sampling distributions of relevant statistics.     

Bias reduction of the maximum-likelihood estimator for a conditional Gaussian MA(1) model

In this paper, we consider an estimation for the unknown parameters of a conditional Gaussian MA(1) model. In the majority of cases, a maximum-likelihood estimator is chosen because the estimator is consistent. However, for small sample sizes the error is large, because the estimator has a bias of O(n^{? 1}). Therefore, we provide a bias of O(n^{? 1}) for the maximum-likelihood estimator for the conditional Gaussian MA(1) model. Moreover, we propose new estimators for the unknown parameters of the conditional Gaussian MA(1) model based on the bias of O(n^{? 1}). We investigate the properties of the bias, as well as the asymptotical variance of the maximum-likelihood estimators for the unknown parameters, by performing some simulations. Finally, we demonstrate the validity of the new estimators through this simulation study.     

基于休闲经济发展的城市休闲空间体系及其拓展

城市人口的迅速膨胀和信息社会的多元交流共同造就了大众休闲文化,而城市休闲空间则为这一文化的交流与扩散提供有效载体,并在促进经济增长与社会发展等方面均具有重要意义。整个社会的休闲消费特质必然对城市休闲空间的发展产生深远影响,而休闲经济的快速发展也使城市休闲空间拥有了更多的内容和形式。通过城市休闲空间的实践考察,可以发现:从社区公共休闲空间—市区休闲组团空间—环城延展休闲空间—城郊多维休闲空间,发展到虚拟动态休闲空间,现代城市已经形成一个多层次的开放式城市休闲空间体系。随着休闲经济的发展,未来城市休闲空间将向着综合性与集中化、体验性与人性化、健康性与生态化、文化性与特色化的方向拓展。     

论当前中国高等教育、就业现状和代际公平

一个国家的教育政策决定了该国长远的人才培养。然而,中国目前的高等教育政策已经为社会人士所诟病,其庞大的扩招规模,不仅长期影响国家的宏观就业政策,而且导致了严重的社会问题。文章将从＂代际公平＂的视角,对当前的人才培养政策的弊端进行分析,并指出为了构建和谐社会,限制或者稳定招生人数才是当前高等教育惟一正确举措。     

类似必要共同诉讼的产生与适用——兼论连带债务的共同诉讼形态

基于对合一确定的不同把握,大陆法系诉讼理论对类似必要共同诉讼适用范围的界定也有所不同,但总体呈现出由扩张到限制的发展方向,其类似必要共同诉讼的发展历程和制度构成值得借鉴。我国对类似必要共同诉讼的引入与连带债务诉讼密切相关,导致这一制度呈现扩张适用的现象,有违实体法和诉讼法原理。宜将连带债务的诉讼形态确定为普通共同诉讼,将类似必要共同诉讼的适用范围限定于既判力扩张的基础上,并以此为基础构建其理论体系和程序规则。