黄国雄教授商品流通理论研究回顾

黄国雄教授是中国现代商品流通理论的奠基人.文章通过文献梳理和实际观察,对黄国雄教授的商品流通理论研究进行了回顾.黄国雄教授的商品流通理论主要包括三个方面:在内容方面,聚焦于商品流通理论的研究;在方法方面,多使用经验主义范式;在主要贡献方面,奠定了现代商学理论的根基,具体包括提出了“商业创造价值论”“流通产业基础论”和“商道即人道论”等三大理论,以及建立了现代商学的学科体系.     

安徽教育投资发展状况及优化路径研究

近年来，安徽省教育投资环境明显改善，大力推进教育均衡发展，教育投资总量不断增加，教育事业稳步发展，但同时也暴露出诸多问题。安徽省教育投资发展存在的主要问题是：教育投资增速放缓，多元化投资渠道尚未形成，投资结构不合理，区域分布不均衡。在“十九大”提出的教育发展精神指导下，针对安徽省教育投资发展现状与存在的主要问题，提出优化安徽省教育投资发展的主要路径。     

初次收入分配对我国居民旅游消费影响研究

文章选取初次分配工资分配率、城镇居民基尼系数、各收入阶层旅游消费占比作为分析指标，经分析研究所得结论为：低、中收入阶层旅游消费占比与分配率负相关，分配率所代表的初次收入分配总量性影响对中收入阶层旅游消费影响较大，中、高收入阶层旅游消费占比都与城镇居民基尼系数正相关，其中城镇居民基尼系数代表的初次收入分配结构性影响对高收入阶层旅游消费影响较大；并针对各阶层的特殊性对各收入阶层旅游消费发展提出相应政策建议.     

中国商品流通现代化的模糊综合评价研究

完善了商品流通现代化指标体系,并运用模糊综合法将商品流通现代化指标转换成了指数。研究发现商品流通现代化水平与其区域位置、总体经济实力密切相关,且受到产业结构的影响。同时,通过基于指数的聚类分析得到如下结论:"率先发展型区域"要抓住历史机遇,培养创新型人才,保持领先地位;"积极追赶型区域"要发挥地理优势,打破省之间、行业和部门之间、所有制之间的界限,积极参与各种区域经济合作;"流通欠发达型区域"要进一步推进和实施西部大开发战略,推进物流现代化。     

清代广西三界庙地理分布与三界神信仰探析

三界庙在清代广西各府中都有分布,主要集中分布在南宁、浔州、郁林、平乐、梧州等五府(州),呈现出一种沿着西江流域自东向西递减的分布规律,在郁江两岸分布最为密集。清代广西已经初步形成了三界神祭祀圈,而这种祭祀圈的出现与三界神的现实功利色彩有着密不可分的关系。     

浅析中国土族人口分布格局及其社会发展

人口分布格局,既是人在空间分布上呈现出来的地理状态,又是许多附着于分布环境上的社会人文变量的外在表现,因此对其进行研究具有探索自然和认识社会的双重意义。本文就中国土族人口的分布格局进行探讨,从历史和现实两方面分析了形成这种分布的原因,并在此基础上探讨了与人口分布格局密切相关的人口城市化、人口流动、人口素质提高、区域经济发展等社会热点问题,以期为政府政策的制定提供可靠的依据。     

Orientation relationship in finite dimensional space

In the present article, we discuss the regression of a point on the surface of a unit sphere in d dimensions given a point on the surface of a unit sphere in p dimensions, where p may not be equal to d. Point projection is added to the rotation and linear transformation for regression link function. The identifiability of the model is proved. Then, parameter estimation in this set up is discussed. Simulation studies and data analyses are done to illustrate the model.     

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.     

Bayesian inference for zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables

Abstract

In the fields of internet financial transactions and reliability engineering, there could be more zero and one observations simultaneously. In this paper, considering that it is beyond the range where the conventional model can fit, zero-and-one-inflated geometric distribution regression model is proposed. Ingeniously introducing Pólya-Gamma latent variables in the Bayesian inference, posterior sampling with high-dimensional parameters is converted to latent variables sampling and posterior sampling with lower-dimensional parameters, respectively. Circumventing the need for Metropolis-Hastings sampling, the sample with higher sampling efficiency is obtained. A simulation study is conducted to assess the performance of the proposed estimation for various sample sizes. Finally, a doctoral dissertation data set is analyzed to illustrate the practicability of the proposed method, research shows that zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables can achieve better fitting results.     

Risk aggregation with dependence and overdispersion based on the compound Poisson INAR(1) process

Abstract

This paper considers an extension of the classical discrete time risk model for which the claim numbers are assumed to be temporal dependence and overdispersion. The risk model proposed is based on the first-order integer-valued autoregressive (INAR(1)) process with discrete compound Poisson distributed innovations. The explicit expression for the moment generating function of the discounted aggregate claim amount is derived. Some numerical examples are provided to illustrate the impacts of dependence and overdispersion on related quantities such as the stop-loss premium, the value at risk and the tail value at risk.