试论苯教的宗教性质及与藏区民间宗教的关系

文章主要对藏区古老的苯教的宗教属性和特征进行分析,认为在历史上,苯教作为一种宗教的名称出现时,其已经形成系统性的宗教体系,所以我们不能将之列为原始宗教。苯教一般被分为两类,即斯巴苯教与雍仲苯教。其中斯巴苯教与民间宗教有密切的关系,为藏区民间宗教的源流之一。     

礼物、关系和信任

本文试图通过关系/人情/信任三个具有本土意义的重要概念,来探讨中国社会中的礼物流动及其背后所隐含的人际意义。通过理论建构,笔者提出了一个“人际关系在送礼过程中的运作过程”模型。     

略论19世纪70-90年代的中国木船业

在近代,随着外国侵略势力的到来,中国航运业受到了很大的冲击,中国古老的航运工具———木船受到了排挤。但由于各种原因,在19世纪70—90年代这段时间里中国木船业并没有走向灭亡,而是持续的向前发展。     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

Semiparametric regression of clustered current status data

This paper discusses regression analysis of clustered current status data under semiparametric additive hazards models. In particular, we consider the situation when cluster sizes can be informative about correlated failure times from the same cluster. To address the problem, we present estimating equation-based estimation procedures and establish asymptotic properties of the resulting estimates. Finite sample performance of the proposed method is assessed through an extensive simulation study, which indicates the procedure works well. The method is applied to a motivating data set from a lung tumorigenicity study.     

Hard thresholding regression

In this paper, we propose the hard thresholding regression (HTR) for estimating high‐dimensional sparse linear regression models. HTR uses a two‐stage convex algorithm to approximate the ?₀‐penalized regression: The first stage calculates a coarse initial estimator, and the second stage identifies the oracle estimator by borrowing information from the first one. Theoretically, the HTR estimator achieves the strong oracle property over a wide range of regularization parameters. Numerical examples and a real data example lend further support to our proposed methodology.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

高维大数据基因网络中的社区发现——以NC方法为例

从大量基因中识别出致病基因是大数据下的一个十分重要的高维统计问题。基因间网络结构的存在使得对于致病基因的识别已从单个基因识别扩展到基因模块识别。从基因网络中挖掘出基因模块就是所谓的社区发现（或节点聚类）问题。绝大多数社区发现方法仅利用网络结构信息,而忽略节点本身的信息。Newman和Clauset于2016年提出了一个将二者有机结合的基于统计推断的社区发现方法（简称为NC方法）。本文以NC方法为案例,介绍统计方法在实际基因网络中的应用和取得的成果,并从统计学角度提出了改进措施。通过对NC方法的分析可以看出对于以基因网络为代表的非结构化数据,统计思想和原理在数据分析中仍然处于核心地位。而相应的统计方法则需要针对数据的特点及关心的问题进行相应的调整和优化。     

REAL EXCHANGE RATE,MONETARY POLICY,AND THE U.S. ECONOMY: EVIDENCE FROM A FAVAR MODEL

This paper examines the effects of exchange rate depreciation to the U.S. economy in a factor‐augmented vector autoregression model using monthly data of 148 variables for the post–Bretton Woods period of 1973–2017. Exchange rate shock is identified to reflect exogenous disturbances to the foreign exchange market, and movements in exchange rate that are not accounted for by changes in the U.S. monetary policy. We find that depreciation is expansionary and inflationary to the broad U.S. economy, the current account improves over time conforming to the J‐curve theory, and monetary policy is leaning against the wind. (JEL E3, E5, F31, F32, F41)     

Does a More Diversified Revenue Structure Lead to Greater Financial Capacity and Less Vulnerability in Nonprofit Organizations? A Bibliometric and Meta-Analysis

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This article explores how and to what extent revenue diversification and concentration strategies affect financial...