基于“大事件”驱动的城市国际化战略研究

世界城市发展的规律和经验表明,以大型赛事、会展为支撑的大事件对提升城市国际竞争力有着重要的意义。本文通过对大事件与城市联动发展的机理分析,指出其对于创新城市发展主题、创新城市空间结构体系、创新城市产业体系以及创新城市文化属性的作用方式,进而通过比较借鉴中外城市的成功经验,对承办2014年青奥会的南京,提出了提升城市国际化功能的战略性方向和政策性建议。     

1872年马里亚老士号事件研究--近代中日关系史上的一件往事

秘鲁货船马里亚老士号因拐运华工在日本横滨被扣并引起一场国际诉讼,日本外务大臣副岛种臣亲自指挥,神奈川县长官大江卓自任审判长,终于将被拐华工全部解救出来,并通知清政府派人赴日将其接回中国;而包括李鸿章在内的清政府大员也相当重视此事,公文往来,讨论处理办法,积极回应,并派遣上海地方官员陈福勋赴日将被拐华工带回中国.陈福勋即成为近代中国第一个赴日办理交涉的外交使节,而该事件使刚刚起步的近代中日关系第一次产生良性互动,意义重大.     

胡适与"九一八"事变

“九一八”事变之前胡适对东北形势就很关注,并对危机的出现有预感。事变后,他积极投身于拯救民族危机的活动中。他与友人创办《独立评论》,并在主编位置上为之付出巨大的心血。他从国际、国内等方面着眼,对事变的原因、影响所做的阐释,许多是有见地、有的甚至是很精辟的。胡适在“九一八”事变后的爱国行动也对他的人生道路产生了深刻的影响。     

Dynamic path analysis—a new approach to analyzing time-dependent covariates

In this article we introduce a general approach to dynamic path analysis. This is an extension of classical path analysis to the situation where variables may be time-dependent and where the outcome of main interest is a stochastic process. In particular we will focus on the survival and event history analysis setting where the main outcome is a counting process. Our approach will be especially fruitful for analyzing event history data with internal time-dependent covariates, where an ordinary regression analysis may fail. The approach enables us to describe how the effect of a fixed covariate partly is working directly and partly indirectly through internal time-dependent covariates. For the sequence of times of event, we define a sequence of path analysis models. At each time of an event, ordinary linear regression is used to estimate the relation between the covariates, while the additive hazard model is used for the regression of the counting process on the covariates. The methodology is illustrated using data from a randomized trial on survival for patients with liver cirrhosis.     

时评传播范围的拓展与传播方式的丰富——本世纪初与二十世纪初报纸时评之比较

本世纪初与二十世纪初报纸时评相比较,传播范围有了很大拓展,传播方式更为丰富,从而更好地满足了当今社会的需要。这些发展变化归因于经济发展、科教进步、交通发达、社会民主、互联网普及等,也归因于读者需要、媒体竞争等。     

CPI的发布对中国股票市场收益率影响的实证研究——以上海股票市场为例

在引起股票市场波动的众多因素中,消费价格指数CPI日渐引起人们的关注。本文采用事件研究法,以上海证券市场为例。重点研究CPI这一宏观经济信息的公布对中国股票市场的短期影响效应。结果发现,在CPI公布前较短的时间内,股票市场出现显著的异常收益,这表明中国股市有效性不足,重要的经济信息发布前可能存在泄露问题．事后股市对信息的消化吸收也相对缓慢。在稍长的事件期内,CPI信息的发布对股市的累积公告效应较小．市场主体预期的调整和多空双方力量的对比抵消了各事件日内股市受到的信息的冲击。     

Information-statistical pattern based approach for data mining

This paper presents information theory and statistical analysis as two fundamental conceptual tools for data mining. A data mining technique based on these two conceptual tools consists of three steps. The first step is a statistical approach for discovering data patterns. The second step is an information-theoretic approach for identifying models that encapsulate the statistical behavior of the data patterns. The last step is a probabilistic approach for pattern-based inference that uncovers unknown significant event patterns.     

The impact of financial sanctions: The case of Iran

This study provides a detailed analysis of the impact of financial sanctions on publicly traded companies. We consider the effect of imposing and lifting sanctions on the target country’s traded equities and examine the differences in the reaction of politically connected firms and those without such connections. The paper focuses on Iran due to (1) its sizable financial markets, (2) imposition of sanctions of varying severity and duration on private and state-owned companies, (3) the significant presence of politically connected firms in the stock market, and (4) the unique event of the 2015 nuclear deal, resulting in fairly rapid lifting of a sizable portion of imposed sanctions. We find that sanctions affect politically connected firms more than ordinary firms, have lasting negative effects on profitability ratios, and that politically connected firms stock prices bounce back more slowly after removal of sanctions. Firms targeted by financial sanctions decrease their leverage and increase their cash holding to manage their perceived increase in risk profile.     

Assessing circular error probable when the errors are elliptical normal

An important problem of continuing interest to engineers is the need to assess the circular error probable (CEP), a measure of the impact accuracy of a projectile or a measure of GPS point positioning accuracy. One of the challenges in addressing this problem is to construct some accurate confidence bounds or intervals for CEP in the small sample settings, where certain amount of systematic biases exist in testing experiments. Currently there is no general method available to deal with this challenge due to the intractability of the distributions of the existing CEP estimators. In this paper, in order to meet this challenge, several new approximate formulas are derived for calculating CEP, which are more accurate than the existing ones but still simple to use. Both exact and empirical expressions for the derivatives of CEP with respect to the population means and variances are also given. Using these formulas, three kinds of confidence bounds or intervals for CEP are proposed, which are based on the parametric bootstrap, the asymptotic distribution, and the Cornish–Fisher expansion, respectively. Moreover, a bias-corrected estimator of CEP is provided. The performances of these procedures are evaluated based on some Monte Carlo simulation studies. Both the theoretical and simulation results show that the Cornish–Fisher expansion-based procedure performs slightly better than the other two procedures when the downrange and cross-range variances are assumed the same. However, when these two variances are different, the simulation demonstrates that the bootstrap approach can be superior to the Cornish–Fisher for the small samples (say n=10), and vice versa for the moderate samples (say n=20).     

Joint modelling of event counts and survival times

Summary.  In studies of recurrent events, such as epileptic seizures, there can be a large amount of information about a cohort over a period of time, but current methods for these data are often unable to utilize all of the available information. The paper considers data which include post-treatment survival times for individuals experiencing recurring events, as well as a measure of the base-line event rate, in the form of a pre-randomization event count. Standard survival analysis may treat this pre-randomization count as a covariate, but the paper proposes a parametric joint model based on an underlying Poisson process, which will give a more precise estimate of the treatment effect.