区域旅游资源综合评价方法改进途径探讨

随着旅游规划开发观念和模式的变化与发展,相应的综合评价工作应从观念、内答和万法上进行改进,以满足旅游规划开发的需要。具体探讨了3个改进途径：创新观念,构建评价指标体系;利用灰色统计处理专家评估意见;以灰色聚类分析评价模型取代加权平均法获得综合评价值及多层次评价结果。文中论述了旅游规划开发观念模式所经历的3个阶段,并提出与之相适应的3类综合评价指标体系。在评价观念和方法上均具有创新性。     

企业计划统计信息管理系统的流程再造

为更好地借助信息化手段,实现采购过程中的信息共享,实现供应链企业之间的信息有效传递,确保信息传递的效率和质量,实现数据的统计和利用,营销公司要引进先进的管理平台,将最新的理论和技术应用于企业运营管理当中。论文阐述了现阶段企业进行流程再造和信息系统升级改造的必要性,结合上下游供应链管理的情况,对供应链管理各节点存在的问题进行了分析,最后有针对性地编制了流程再造的实施方案。     

基于“科技统计”文献数据的LDA主题模型应用研究

在互联网背景下，文献数据爆炸式增长的态势向人们有效获取信息和知识的路径发出挑战。本文以“科技统计”文献数据为研究对象，探讨该类数据预处理、统计描述、关键词挖掘的流程和方法，并进一步应用LDA主题模型，从理论和应用角度为“科技统计”文献数据挖掘提供研究思路和方法支持。     

Are You All Normal? It Depends!

The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the multivariate setting and first review the recent advances in multivariate normality tests for i.i.d. data, with emphasis on the skewness and kurtosis approaches. We show through simulation studies that some of these tests cannot be used directly for testing normality of spatial data. We further review briefly the few existing univariate tests under dependence (time or space), and then propose a new multivariate normality test for spatial data by accounting for the spatial dependence. The new test utilises the union-intersection principle to decompose the null hypothesis into intersections of univariate normality hypotheses for projection data, and it rejects the multivariate normality if any individual hypothesis is rejected. The individual hypotheses for univariate normality are conducted using a Jarque–Bera type test statistic that accounts for the spatial dependence in the data. We also show in simulation studies that the new test has a good control of the type I error and a high empirical power, especially for large sample sizes. We further illustrate our test on bivariate wind data over the Arabian Peninsula.     

An interview with Luis Raúl Pericchi

Luis Raúl Pericchi Guerra was born in Caracas, Venezuela, on 11 March 1952. He completed a B.S. in Mathematics in 1975 at the Universidad Simón Bolívar in Caracas, an M.S. in Statistics at the University of California Berkeley in 1978 and a Ph.D. in Statistics at Imperial College London in 1981. After graduating from Imperial College, Luis Raúl went back to Universidad Simón Bolívar. There, he played a key role in the developing of graduate programmes in Statistics and single handedly built an internationally recognised group focused on Bayesian statistics. In 2001, he moved to the Universidad de Puerto Rico in Rio Piedras to become the Chair of the Mathematics Department. At Universidad de Puerto Rico, he was instrumental in the establishment of a Ph.D. track in Computational Mathematics and Statistics. Luis Raúl has published over 120 papers in statistical and domain-specific journals, making significant contributions to several areas of Bayesian statistics (especially in the areas of model selection and Bayesian robustness) and their application (especially in hydrology). He is a Fellow of the American Statistical Association, the International Society for Bayesian Analysis, the John Simon Guggenheim Memorial Foundation and an Elected Member of the International Statistical Institute. This conversation took place over multiple sessions during the 2022 O'Bayes meeting in Santa Cruz, California, and the months that followed.     

Multi-population mortality projection: The augmented common factor model with structural breaks

Multi-population mortality forecasting has become an increasingly important area in actuarial science and demography, as a means to avoid long-run divergence in mortality projections. This paper aims to establish a unified state-space Bayesian framework to model, estimate, and forecast mortality rates in a multi-population context. In this regard, we reformulate the augmented common factor model to account for structural/trend changes in the mortality indexes. We conduct a Bayesian analysis to make inferences and generate forecasts so that process and parameter uncertainties can be considered simultaneously and appropriately. We illustrate the efficiency of our methodology through two case studies. Both point and probabilistic forecast evaluations are considered in the empirical analysis. The derived results support the fact that the incorporation of stochastic drifts mitigates the impact of the structural changes in the time indexes on mortality projections.     

海南省旅游统计数据的ARIMA模型预测分析和新冠肺炎疫情影响分析

以海南省旅游统计数据为基础，利用ARIMA模型，并结合新冠肺炎确诊人数进行预测分析。结果表明，海南省游客流量和旅游收入在新冠疫情下依然可以很大程度保持原有的增长趋势和季节性波动规律，但两组数据表现略有不同。新冠肺炎疫情对海南省旅游统计数据的影响体现在两个方面，一方面是新冠肺炎疫情严重程度与旅游统计数据存在明显此消彼长的关系，另一方面是新冠肺炎疫情主要对旅游统计数据中的游客流量产生了较大影响。可以得出政策启示：政府要做好疫情防控和让民众对旅游业持有信心，要随着疫情发展弹性放开或收紧旅游业，还要对需求侧进行扶持和对供给侧进行创新。     

Pro-cyclicality beyond business cycle

We show that pro-cyclicality is inherent in risk measure estimates based on historical data. Taking the example of VaR, we show that the empirical VaR measure is mean-reverting over a 1-year horizon when the portfolio is held fixed. It means that a capital requirement rule based on historical measurements of VaR tends in calm times to understate future required capital and tends in volatile times to overstate it. To quantify this pro-cyclicality, we develop a simple and efficient methodology, which we apply to major equity market indices. We make the interesting point that the pro-cyclicality property holds true even in a world with constant volatility, though the empirical magnitude of the mean-reversion is greater than what would be observed in that special case.     

“数字化翅膀”如何推动上市公司高质量发展——来自公司违规治理的经验证据

基于2011—2018年A股上市公司样本,借助爬虫技术构造企业数字化发展程度指标,探讨数字化转型对公司违规的影响。研究发现：数字化转型显著降低了公司违规概率。进一步研究表明：数字化转型显著降低了公司违规倾向,显著增加了公司违规后被稽查的概率;优化内部治理机制（提高内部控制水平、信息披露质量和管理者能力）与强化外部监督水平（提高分析师关注度和长期机构持股水平）是数字化转型降低公司违规概率的重要途径;数字化转型对公司违规的治理效应在司法水平较高、数字金融发展较好地区更显著。本文丰富了企业数字化转型在公司治理领域的经济后果,为政府相关部门进一步推进企业数字化转型、有效治理公司违规提供了经验参考。