汉代乐舞中的人本主义精神探析

在我国长达3000多年的乐舞文化中,汉代乐舞以其张扬阔大、发扬蹈厉的强悍风格独树一帜,堪称中华乐舞史的最强音.造就强汉之音的精神底座,是在秦汉代际蓬勃兴起的人本主义精神.从汉武时期乐制的更作和乐府制度的确立,到盛行两汉的乐舞形制,再到志尽于诗、音尽于曲的乐舞歌诗和意气相尚、悲歌厉响的审美取向,无不反映并凸显着人本主义精神的光辉.在我国当下民族复兴的进程中,人本主义精神同样应成为时代诉求.     

Bowling Alone in Taiwan? Political Trust and Civic Participation of Taiwanese and Their Appraisal of Liberal Democracy and Personal Wellbeing

Social Indicators Research - Based on a nationwide survey dataset collected by Taiwan’s Executive Yuan (2017) (N?=?2737) and adopting structural equation modelling with...     

“功能性表达性模型”与中国警察信任——基于全国样本的多重中介与调节模型分析

功能性模型认为，提升警察信任的主要方法在于预防和控制犯罪，降低犯罪率与提升公众安全感；而表达性模型认为，提升警察信任更应注重维护社会正义与社区凝聚力。基于提出促进中国警察信任度提高的理论模型，提升公众获得感、幸福感、安全感，实现国家和社会整体安全的最终目的，采用结构方程模型验证“功能性-表达性模型”与中国警察信任关系的结果表明，功能性模型与表达性模型内部因素之间存在显著路径关系，同时功能性模型显著影响表达性模型指标进而改变公众对警察的信任程度。经调节效应分析后发现，主观社会阶层负向调节被害经历到犯罪恐惧感的直接路径，社交媒体使用负向调节犯罪恐惧感到警察信任的直接路径；在主观社会阶层和社交媒体使用的共同调节作用下，“被害经历→犯罪恐惧感→警察信任”中介路径效应值，随社交媒体使用频率的提升而增大，随主观社会阶层的升高而减小。     

Genetic distance,international experience and the performance of cross-border R&D for EMNEs

Cross-border R&D can contribute to the enhancement of independent innovation capabilities of emerging markets multinational enterprises (EMNEs) by benefiting from knowledge management. However, scant research exists examining the location impact of cross-border R&D for EMNEs on performance implications. This paper fills this important theoretical gap by building upon the literature of genetic distance in connection with knowledge management. We use a panel data of Chinese high-tech listed companies to empirically examine the impact of genetic distance on the performance of cross-border R&D and the role played by international experience. Our results reveal a positive relationship between genetic distance and the performance of cross-border R&D. Importantly, we highlight the motivation for cross-border R&D of EMNEs to acquire technical knowledge magnifies the positive effects of genetic distance and performance. Furthermore, our analysis indicates that international experience significantly enhanced the positive effect of genetic distance on cross-border R&D performance. We conclude this paper by discussing theoretical contributions to genetic distance, international management and knowledge management, as well as practical implications for cross-border R&D of EMNEs.     

网络民族志基于研究者身份的伦理关系

民族志一度是研究者专业生产他者知识的手段与途径,但在反思传统与顺应新潮流——互联网的基础上,我们发现,一直以来研究者自己也是民族志的研究对象,互联网兴起之后,这种倾向更加明显.网络时代的到来促使研究者的身份发生了显著转变:由自己是主体,被研究者是他者,转变为双方是平等的交互主体;由认识主体转变为实践主体;由主动的实践者转变为静默的潜伏者等.网络民族志中研究者身份的变化,为新的研究伦理关系的出现开辟了道路,即研究者与被研究者既是学术研究的手段,也是学术研究的目的.     

Semiparametric Bayesian analysis for longitudinal mixed effects models with non-normal AR(1) errors

Statistics and Computing - This paper studies Bayesian inference on longitudinal mixed effects models with non-normal AR(1) errors. We model the nonparametric zero-mean noise in the autoregression...     

大数据背景下网络调查样本的建模推断问题研究——以广义Boosted模型的倾向得分推断为例

随着大数据和网络的不断发展，网络调查越来越广泛，大部分网络调查样本属于非概率样本，难以采用传统的抽样推断理论进行推断，如何解决网络调查样本的推断问题是大数据背景下网络调查发展的迫切需求。本文首次从建模的角度提出了解决该问题的基本思路：一是入样概率的建模推断，可以考虑构建基于机器学习与变量选择的倾向得分模型来估计入样概率推断总体；二是目标变量的建模推断，可以考虑直接对目标变量建立参数、非参数或半参数超总体模型进行估计；三是入样概率与目标变量的双重建模推断，可以考虑进行倾向得分模型与超总体模型的加权估计与混合推断。最后，以基于广义Boosted模型的入样概率建模推断为例演示了具体解决方法。     

Regression analysis of case-cohort studies in the presence of dependent interval censoring

The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators is established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.     

A primal dual active set with continuation algorithm for high-dimensional nonconvex SICA-penalized regression

The smooth integration of counting and absolute deviation (SICA) penalty has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection and parameter estimation. However, solving the nonconvex optimization problem associated with SICA penalty in high-dimensional setting remains to be enriched, mainly due to the singularity at the origin and the nonconvexity of the SICA penalty function. In this paper, we develop a fast primal dual active set (PDAS) with continuation algorithm for solving the nonconvex SICA-penalized least squares in high dimensions. Upon introducing the dual variable, the PDAS algorithm iteratively identify and update the active set in the optimization using both the primal and dual information, and then solve a low-dimensional least square problem on the active set. When combined with a continuation strategy and a high-dimensional Bayesian information criterion (BIC) selector on the tuning parameters, the proposed algorithm is very efficient and accurate. Extensive simulation studies and analysis of a high-dimensional microarray gene expression data are presented to illustrate the performance of the proposed method.