Estimation from Zero-Failure Data

When performing quantitative (or probabilistic) risk assessments, it is often the case that data for many of the potential events in question are sparse or nonexistent. Some of these events may be well-represented by the binomial probability distribution. In this paper, a model for predicting the binomial failure probability, P , from data that include no failures is examined. A review of the literature indicates that the use of this model is currently limited to risk analysis of energetic initiation in the explosives testing field. The basis for the model is discussed, and the behavior of the model relative to other models developed for the same purpose is investigated. It is found that the qualitative behavior of the model is very similar to that of the other models, and for larger values of n (the number of trials), the predicted P values varied by a factor of about eight among the five models examined. Analysis revealed that the estimator is nearly identical to the median of a Bayesian posterior distribution, derived using a uniform prior. An explanation of the application of the estimator in explosives testing is provided, and comments are offered regarding the use of the estimator versus other possible techniques.     

出口二元边际、行业异质性与劳动生产率

基于行业异质性视角,文章主要考察出口二元边际对劳动生产率的影响.利用UN Comtrade数据库,采用HK指数法测度中国出口深度边际和出口广度边际,发现中国出口产品种类基本覆盖了世界全部种类的90％.进一步地,借助聚类分析方法综合多种因素区分行业劳动技能异质性,出口二元边际与劳动生产率关系的实证结果显示:总体上出口深度边际显著推动劳动生产率提高,出口广度边际对劳动生产率产生抑制作用.更为重要的是,出口深度边际对劳动生产率的促进作用仅存在于高技能行业,受限于行业产品技术含量和劳动技能水平等,低技能和中等技能行业出口深度边际出现贫困化增长现象.出口种类扩张的生产率损失超过出口溢出效应的生产率收益,出口广度边际对高中低技能行业劳动生产率具有不同程度的负向影响.     

企业孵化器绩效评价研究综述

国外主要是通过大量调查,对BI运行的效果进行研究。这种研究,取得了大量资料,获得了很多有用信息,便于明确政府的政策效果,明确BI的价值与意义。其不足主要表现在理论研究薄弱和系统研究缺乏。国内有关BI的绩效研究涉及了BI发展的许多问题,范围较宽,得到了许多相关BI绩效评价理论与方法的积累,但由于其延用了西方的理论与方法,因而同样存在经验描述多,理论探索少,缺乏系统的理论支撑及相关的绩效评价方法与指标体系。     

青海话中的词缀"头"

青海话中的词缀“头”除了有与汉语普通话词缀“头”相同的特点外,还能够放在动词词根和形容词词根语素的后面,构成一个新的名词,表示某事是否值得做或必要性的主观评价,也可以指对象或状态;这种方式构成的名词是一个开放的系统,只存在于具体语境之中。文章还进一步探讨了这种用法的起源,并与其它方言中的词缀“头”作了对比分析。     

基于重大偏差标准的软件项目工作量管理方法研究

为有效管理软件项目工作量,本文研究了一个基于重大偏差标准的软件项目工作量管理模型。该模型利用过程控制的原理和数理统计的方法,提出依据重大偏差标准和风险储备时间对软件项目进行动态控制,并介绍了风险储备时间与重大偏差标准的定量设置方法。该模型涵盖了从风险储备时间的确定,到重大偏差标准的提出,再根据风险储备时间和重大偏差标准进行监控的整个过程。为提高软件项目管理精度,保证项目在可接受的偏差范围内顺利完成打下坚实的基础。     

加权复合分位数回归方法在动态VaR风险度量中的应用

风险价值(VaR)因为简单直观,成为了当今国际上最主流的风险度量方法之一,而基于时间序列自回归(AR)模型来计算无条件风险度量值在实业界有广泛应用。本文基于分位数回归理论对AR模型提出了一个估计方法--加权复合分位数回归(WCQR)估计,该方法可以充分利用多个分位数信息提高参数估计的效率,并且对于不同的分位数回归赋予不同的权重,使得估计更加有效,文中给出了该估计的渐近正态性质。有限样本的数值模拟表明,当残差服从非正态分布时,WCQR估计的的统计性质接近于极大似然估计,而该估计是不需要知道残差分布的,因此,所提出的WCQR估计更加具有竞争力。此方法在预测资产收益的VaR动态风险时有较好的应用,我们将所提出的理论分析了我国九只封闭式基金,实证分析发现,结合WCQR方法求得的VaR风险与用非参数方法求得的VaR风险非常接近,而结合WCQR方法可以计算动态的VaR风险值和预测资产收益的VaR风险值。     

房价波动、 收入水平对住房消费的影响——基于SYS-GMM估计方法的区域差异分析

基于生命周期-持久收入假说,在一般随机Ramsey模型的基础上,推导包含房价、收入和财富的住房消费函数,利用2002年至2013年31个省直辖市的面板数据,采用两步System-GMM估计方法考察我国房价波动和居民收入水平对住房消费的影响.实证结果表明:房价波动对全国居民住房消费具有显著的抑制作用;其中,滞后期和当期房价波动与当期住房消费负相关,挤出效应明显;未来一期房价波动与住房消费变化方向一致,存在积极的财富效应;此外,房价波动对东中西部各地区居民住房消费的抑制作用存在较大差异,西部地区抑制效应最为明显;滞后期住房消费与当期住房消费变动方向一致,人均可支配收入波动和人均年底储蓄余额对我国人均住房消费都起着重要的支撑作用.     

基于非参数时变Copula模型的美国次贷危机传染分析

金融危机传染分析是国际金融领域中的重要课题,本文对Copula变点检测方法进行推广,采用时变非参数阿基米德Copula模型检验金融危机传染的存在性及其变化趋势,以时变尾部相依系数的大小来度量危机传染程度,并结合系数的变化趋势和时间段对金融危机传染效应进行分析.最后选择全球六个主要股票市场指数和S&P500指数进行危机传染实证研究,得出次贷危机对不同国家或地区的传染效应有所差别.     

A class of weighted dependence measures for bivariate failure time data

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.