“一带一路”背景下中国区域物流网络模型研究

选取“一带一路”重点涉及的五大区域18个地区为研究对象,利用改进引力模型测量区域物流引力大小及地区引力总量,构建区域物流网络模型。在明确现有模型的特点和“一带一路”对物流网络目标要求的基础上,考虑区域物流发展水平和区域间差异情况,给出优化方案及优化后的物流网络模型,指出中国区域物流网络存在的问题并给出合理化建议,旨为政府决策提供参考。     

Ordering results for series and parallel systems comprising heterogeneous exponentiated Weibull components

In this paper, we discuss the usual stochastic and reversed hazard rate orders between the series and parallel systems from two sets of independent heterogeneous exponentiated Weibull components. We also obtain the results concerning the convex transform orders between parallel systems and obtain necessary and sufficient conditions under which the dispersive and usual stochastic orders, and the right spread and increasing convex orders between the lifetimes of the two systems are equivalent. Finally, in the multiple-outlier exponentiated Weibull models, based on weak majorization and p-larger orders between the vectors of scale and shape parameters, some characterization results for comparing the lifetimes of parallel and series systems are also established, respectively. The results of this paper can be used in practical situations to find various bounds for the important aging characteristics of these systems.     

Objective and subjective conditions for the continuity of the Peruvian extractive development model

ABSTRACT

The Peruvian economy depends for its growth on the export of its mineral resources. The core of the current economic development model is the export of the country's commodities and a free and (relatively) unregulated functioning of the markets. We argue that the continuity of the country's extractive development model is the consequence of the interplay between the objective and subjective conditions of Peruvian capitalist development. The analysis of the objective conditions is focused on the role of the country in the international division of labour, the relation between this role and the country’s economic and business structure, and the functionality of the extractive development model for the Peruvian State. In the case of the subjective conditions, the strength of the political Left, the labour movement and the social movements against mining capital are analyzed     

Joint modelling of longitudinal binary data and survival data

The medical costs in an ageing society substantially increase when the incidences of chronic diseases, disabilities and inability to live independently are high. Healthy lifestyles not only affect elderly individuals but also influence the entire community. When assessing treatment efficacy, survival and quality of life should be considered simultaneously. This paper proposes the joint likelihood approach for modelling survival and longitudinal binary covariates simultaneously. Because some unobservable information is present in the model, the Monte Carlo EM algorithm and Metropolis-Hastings algorithm are used to find the estimators. Monte Carlo simulations are performed to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. Real data are used to demonstrate the feasibility of the proposed model.     

城市人力资本外部性的识别——基于工业企业生产率视角的研究

人力资本外部性的存在具有重要的现实意义,它是政府进行教育投资和人力资本投资的重要决策依据.文章从工业企业生产率视角识别城市人力资本外部性,基于中国工业企业微观数据、人口普查分县资料和地级市统计数据,考虑城市人力资本变量的内生性后,采用两阶段最小二乘法,实证研究城市人力资本对工业企业生产率的影响.研究发现:在控制企业自身的人力资本后,城市大专以上学历人口占比越高,工业企业的产出越高,人力资本的溢出效应显著,且人力资本正外部性主要存在于人口规模300万以上的大城市.从工业内部看,行业内人力资本正外部性随城市规模扩大逐渐增强,而行业外人力资本正外部性随城市规模扩大逐渐减弱.从企业异质性看,高新技术企业、非国有企业和大企业从城市人力资本提高中获得的正向溢出效应更大.此外,城市外商直接投资和地方政府财政支出越高,地理区位距离港口越远,越不利于工业企业产出的提高.文章的研究结论表明增加人力资本投资,促进人力资本结构升级,发挥大城市人力资本溢出效应对新时期经济可持续增长尤为重要.     

网络视频实时在线评论用户认可度的影响因素

目前，网络视频实时在线评论激增带来的信息爆炸，使实时在线评论信息管理成为重要议题。以往对实时在线评论的研究多集中于个人行为结果方面，而缺乏对实时在线评论信息管理问题的关注；同时，现有视频网站对实时在线评论信息的管理也存在诸多局限。以爱奇艺视频网站热度榜中的电影为样本，基于信息质量维度框架并采用Tobit模型对实时在线评论用户认可度的影响因素进行研究，结果发现：实时在线评论的新颖性、相似性、情感倾向性和信息丰富性，均对实时在线评论的用户认可度存在显著影响，其中新颖性和相似性影响作用最大。视频网站可以据此进一步强化实时在线评论管理，改善用户的体验，保持用户的粘性。     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Cox regression of clustered event times with covariates missing not at random

Motivated by a recent tuberculosis (TB) study, this paper is concerned with covariates missing not at random (MNAR) and models the potential intracluster correlation by a frailty. We consider the regression analysis of right‐censored event times from clustered subjects under a Cox proportional hazards frailty model and present the semiparametric maximum likelihood estimator (SPMLE) of the model parameters. An easy‐to‐implement pseudo‐SPMLE is then proposed to accommodate more realistic situations using readily available supplementary information on the missing covariates. Algorithms are provided to compute the estimators and their consistent variance estimators. We demonstrate that both the SPMLE and the pseudo‐SPMLE are consistent and asymptotically normal by the arguments based on the theory of modern empirical processes. The proposed approach is examined numerically via simulation and illustrated with an analysis of the motivating TB study data.     

Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset.     

A Factor-Adjusted Multiple Testing Procedure With Application to Mutual Fund Selection

In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors, for the purpose of selecting skilled funds in empirical finance. The factor-adjusted p-values were obtained after extracting the latent common factors by the principal component method. Under some mild conditions, the false discovery proportion can be consistently estimated even if the idiosyncratic errors are allowed to be weakly correlated across units. Furthermore, by appropriately setting a sequence of threshold values approaching zero, the proposed FAT procedure enjoys model selection consistency. Extensive simulation studies and a real data analysis for selecting skilled funds in the U.S. financial market are presented to illustrate the practical utility of the proposed method. Supplementary materials for this article are available online.