元代回族诗人萨都剌与道教

元代萨都刺《雁门集》中保存有数十首涉道诗,是萨都刺寻访江南道教的历史记录。萨都刺作为东来的回回人,注意了解中国传统的儒释道文化。他足迹遍及江南道教名山宫观,与道门人士结为方外之友,这反映出在多元一体的中国社会中,不同民族宗教间的对话与交流。     

关敬吾论日本传统故事的类型与结构

本文是日本民俗学家关敬吾先生对日本传统故事的概念界定以及对之内容、结构特点的概括,作为“他山之石”,可以给中国民俗学理论研究提供参考。     

语言唤醒与《格萨尔》“神授”现象

中外学者很早就有人提出“语言唤醒”一词,语言唤醒究其实质来说就是在内外环境的刺激或激发下,语言潜力被开发出来的过程。从本质上说心理唤醒和语言唤醒表述了相似的内容。对于《格萨尔》“神授”艺人的神奇语言能力,学界目前仍是众说纷纭。文章认为：从心理唤醒的正向和负向两个方向可以合理解释“神授”艺人们的神奇语言能力。     

奥罗奇人(下)

概括介绍了俄罗斯联邦境内的北方少数民族奥罗奇人的族称、族源、社会经济状况及其演变 ,和奥罗奇人的三界观、灵魂观、鬼神观、图腾崇拜、熊祭和萨满教信仰等     

Modal non‐linear regression in the presence of Laplace measurement error

In this paper, we propose a robust estimation procedure for a class of non‐linear regression models when the covariates are contaminated with Laplace measurement error, aiming at constructing an estimation procedure for the regression parameters which are less affected by the possible outliers, and heavy‐tailed underlying distribution, as well as reducing the bias introduced by the measurement error. Starting with the modal regression procedure developed for the measurement error‐free case, a non‐trivial modification is made so that the modified version can effectively correct the potential bias caused by measurement error. Large sample properties of the proposed estimate, such as the convergence rate and the asymptotic normality, are thoroughly investigated. A simulation study and real data application are conducted to illustrate the satisfying finite sample performance of the proposed estimation procedure.     

A strong law of large numbers for independent random variables under non-additive probabilities

Abstract

Under non‐additive probabilities, cluster points of the empirical average have been proved to quasi-surely fall into the interval constructed by either the lower and upper expectations or the lower and upper Choquet expectations. In this paper, based on the initiated notion of independence, we obtain a different Marcinkiewicz-Zygmund type strong law of large numbers. Then the Kolmogorov type strong law of large numbers can be derived from it directly, stating that the closed interval between the lower and upper expectations is the smallest one that covers cluster points of the empirical average quasi-surely.     

全球价值链嵌入与技术创新——基于生产分解模型的分析

基于增加值和最终产品的生产分解模型,本文对我国制造业前向与后向产业关联下的全球价值链嵌入进行测度,实证分析全球价值链嵌入对技术创新的作用机理,并在此基础上重点探讨了吸收能力与技术差距两个重要情境因素的调节作用,同时,运用双重差分、工具变量法以及GMM动态面板模型进行稳健性检验,以控制潜在的内生性问题。研究发现:①我国制造业通过嵌入全球价值链的国际间知识溢出效应促进技术创新能力的提升;②吸收能力能够强化这一正向影响关系;③技术差距在后向全球价值链嵌入对技术创新的影响关系中呈倒U 型调节作用,而在前向全球价值链嵌入对技术创新的影响关系中呈正向调节作用。本文推动了网络嵌入理论和知识溢出理论从组织网络向全球价值链领域的繁衍,丰富了全球价值链嵌入领域的研究成果,同时为我国制造业企业在参与国际分工过程中利用全球价值链嵌入实现技术创新能力提升提供重要的理论参考。     

中国粮食增产的主要因素贡献分解与实证估算

已有测算中国粮食增产要素贡献率的方法存在虚增或低估某要素贡献率的缺陷,文章通过改进粮食增产贡献率测算方法实证估计了2003—2016年期间单产、播种面积和结构调整对中国粮食增产的贡献。结果表明,中国粮食实现“十二连增”的巨大成就主要是单产增长贡献,年均贡献率50%以上,播种面积和作物内部结构调整也是不可忽视的重要力量,年均贡献率分别为30%与10%以上。然而,未来中国粮食增产依靠播种面积扩大和结构调整的空间变窄,需要探索新的增产路径。     

Evaluating the Use of Spatial Covariance Structures in the Analysis of Cardiovascular Imaging Data

This article investigates the choice of working covariance structures in the analysis of spatially correlated observations motivated by cardiac imaging data. Through Monte Carlo simulations, we found that the choice of covariance structure affects the efficiency of the estimator and power of the test. Choosing the popular unstructured working covariance structure results in an over-inflated Type I error possibly due to a sample size not large enough relative to the number of parameters being estimated. With regard to model fit indices, Bayesian Information Criterion outperforms Akaike Information Criterion in choosing the correct covariance structure used to generate data.     

Robust Smoothed Rank Estimation Methods for Accelerated Failure Time Model Allowing Clusters

Smoothed Gehan rank estimation methods are widely used in accelerated failure time (AFT) models with/without clusters. However, most methods are sensitive to outliers in the covariates. In order to solve this problem, we propose robust approaches based on the smoothed Gehan rank estimation methods for the AFT model, allowing for clusters by employing two different weight functions. Simulation studies show that the proposed methods outperform existing smoothed rank estimation methods regarding their biases and standard deviations when there are outliers in the covariates. The proposed methods are also applied to a real dataset from the “Major cardiovascular interventions” study.