边境口岸城市丹东文化旅游产业发展的灰色关联度分析

边境口岸城市丹东文化旅游资源丰富,但开发利用程度很低。采用灰色关联度分析方法,对边境口岸城市丹东文化旅游产业影响因素进行分析,得出结论：丹东文化旅游产业发展主要受需求因素和资源因素的影响,且资源因素影响程度大于需求因素;口岸因素、经济体量、住宿餐饮业等对丹东文化旅游产业发展影响明显;政府教育支出与文化旅游产业关联度最低;交通、通讯等资源因素在丹东文化旅游产业发展过程中贡献率最大。     

债券流动性与违约风险相关性溢价及实证研究

债券的流动性与违约风险都是影响债券溢价的重要因素,然而以往在违约风险最为突出的公司债券定价中却很少考虑两种风险相关性关系的影响,这与发达市场实证经验不符.在已有研究的基础上同时引入两种风险相关性,通过对Copula函数刻画的不同相关性结构情况下公司债券的收益率和风险变化分析,以及对中国短、中、长期公司债券市场数据的实证检验均发现,流动性与违约风险的相关性之间存在显著的正相关性,且对债券利差具有显著的影响和交互作用.     

文化差异、地理距离对我国各省分国别(地区）出口贸易的影响——基于空间面板数据的实证研究

利用2001-2009年中国30个省分分国别（地区）的出口贸易数据,采用空间探索性数据分析方法分析了各国及地区在我国的出口贸易空间格局,发现各国（地区）在我国的出口贸易中表现出较为显著的空间相关性。进一步,本文构建了考虑空间滞后因子的引力模型实证分析了文化差异和运输距离对我国各省分国别（地区）出口贸易的影响。研究结果表明：我国邻省出口对本省的空间溢出效应随着时间的推移不断增强;而邻省与本省在出口伙伴国选择上的“竞争效应”并不明显;文化差异在各国及地区在我国各省的出口贸易中所起的限制性作用表现突出;而陆运距离对各省分国别（地区）出口贸易的负向影响要明显大于海运距离,且随着时间的推移有不断扩大的迹象。     

非财务信息披露、管控能力和企业业绩

以主板市场上市的非金融、非ST且具有控股子公司的企业集团作为研究样本,借助管理控制系统相关研究的概念和思想,基于样本公司2011-2013年公开披露的各种管理会计相关信息,利用包括监督式机器学习等大数据技术,对会计信息进行量化分析,结合现有的治理和内控指数构建方法的思想,编制出一套反映企业管控能力的指数,并利用公司的财务绩效相关性对其进行效度检验.实证结果发现,该指数不仅具有当期的财务业绩相关性,也具有未来业绩的相关性,比较好地反映了企业的管控能力.     

石家庄市大气颗粒物与能见度相关性研究

运用计量地理学相关分析的方法,以石家庄市2009年12月~2011年1月数据为例,研究大气颗粒物与能见度变化的相关性.研究结果显示:相对湿度与大气能见度呈显著负相关,与颗粒物质量浓度相关性较差;大气颗粒物质量浓度与能见度之间呈负相关,[0.43,0.65)μm粒径范围内二者相关性较显著.     

惠州凹陷典型油气聚集带成藏模式

基于地质、地震、地化等资料,采用流体包裹体分析测试技术,研究惠州凹陷典型油气聚集带的油气富集规律,建立相应的成藏模式。结果表明,文昌组烃源岩是研究区油气的主要贡献者,恩平组烃源岩之油源供给仅限于凹陷内局部区域,油气沿断层短距离垂向运移或沿复合输导体系垂向—侧向长距离运移,经历了两次大规模成藏,据此将惠西—流花油气聚集带的油气成藏模式划分为4 种类型：单源—近距离—垂向运移—多期成藏模式、混源—近距离—垂向运移—多期成藏模式、混源—近距离—垂向—侧向运移—多期成藏模式、单源—远距离—垂向—侧向运移—一期成藏模式;惠陆油气聚集带油气成藏模式为单源—近距离—垂向运移—多期幕式快速成藏模式。     

Effects of correlated covariates on the asymptotic efficiency of matching and inverse probability weighting estimators for causal inference

In observational studies, the overall aim when fitting a model for the propensity score is to reduce bias for an estimator of the causal effect. To make the assumption of an unconfounded treatment plausible researchers might include many, possibly correlated, covariates in the propensity score model. In this paper, we study how the asymptotic efficiency of matching and inverse probability weighting estimators for average causal effects change when the covariates are correlated. We investigate the case with multivariate normal covariates, a logistic model for the propensity score and linear models for the potential outcomes and show results under different model assumptions. We show that the correlation can both increase and decrease the large sample variances of the estimators, and that the correlation affects the asymptotic efficiency of the estimators differently, both with regard to direction and magnitude. Moreover, the strength of the confounding towards the outcome and the treatment plays an important role.     

A Bayesian estimate of the concordance correlation coefficient with skewed data

Concordance correlation coefficient (CCC) is one of the most popular scaled indices used to evaluate agreement. Most commonly, it is used under the assumption that data is normally distributed. This assumption, however, does not apply to skewed data sets. While methods for the estimation of the CCC of skewed data sets have been introduced and studied, the Bayesian approach and its comparison with the previous methods has been lacking. In this study, we propose a Bayesian method for the estimation of the CCC of skewed data sets and compare it with the best method previously investigated. The proposed method has certain advantages. It tends to outperform the best method studied before when the variation of the data is mainly from the random subject effect instead of error. Furthermore, it allows for greater flexibility in application by enabling incorporation of missing data, confounding covariates, and replications, which was not considered previously. The superiority of this new approach is demonstrated using simulation as well as real‐life biomarker data sets used in an electroencephalography clinical study. The implementation of the Bayesian method is accessible through the Comprehensive R Archive Network. Copyright © 2015 John Wiley & Sons, Ltd.     

Notes on estimation of the intraclass correlation under the AB/BA crossover trial

Under the AB/BA crossover trial, we focus our attention on estimation of the intraclass correlation in normal data. We develop both point and interval estimators in closed form for the intraclass correlation. We employ Monte Carlo simulation to study the performance of these estimators in a variety of situations. We note that the estimators developed here for the intraclass correlation remain valid even when there are possibly unexpected carry-over effects.     

A Bivariate Distribution Family with Specified Correlation Coefficient and Given Marginals

For given continuous distribution functions F(x) and G(y) and a Pearson correlation coefficient ρ, an algorithm is provided to construct a sequence of continuous bivariate distributions with marginals equal to F(x) and G(y) and the corresponding correlation coefficient converges to ρ. The algorithm can be easily implemented using S-Plus or R. Applications are given to generate bivariate random variables with marginals including Gamma, Beta, Weibull, and uniform distributions.