Multivariate Kendall's tau for change‐point detection in copulas

Statistical procedures for the detection of a change in the dependence structure of a series of multivariate observations are studied in this work. The test statistics that are proposed are $L_1$ , $L_2$ , and $L_{\infty }$ distances computed from vectors of differences of Kendall's tau; two multivariate extensions of Kendall's measure of association are used. Since the distributions of these statistics under the null hypothesis of no change depend on the unknown underlying copula of the vectors, a procedure based on the multiplier central limit theorem is used for the computation of p‐values; the method is shown to be valid both asymptotically and for moderate sample sizes. Alternative versions of the tests that take into account possible breakpoints in the marginal distributions are also investigated. Monte Carlo simulations show that the tests are powerful under many scenarios of change‐point. In addition, two estimators of the time of change are proposed and their efficiency is carefully studied. The methodologies are illustrated on simulated series from the Canadian Regional Climate Model. The Canadian Journal of Statistics 41: 65–82; 2013 © 2012 Statistical Society of Canada     

抽水蓄能电站建设对经济拉动效应的实证研究

抽水蓄能电站建设可以为项目建设区域带来相应的经济效应．为了科学定量地分析这一经济拉动效应,提出了选用投入产出模型作为抽水蓄能电站建设投资在生产环节经济拉动效应的测算方法,采用了边际消费倾向作为在消费环节由于抽水蓄能电站建设投资使居民收入增加导致消费需求增加而产生乘数效应的计算方法,并对某抽水蓄能电站建设期间为项目建设区域带来的经济效应进行了定量测算．该研究是关于抽水蓄能电站等基础设施建设对经济拉动效应展开定量研究进行的一次有益尝试．     

从《空间与重大灾害国际宪章》看空间技术与国际减灾合作

<空间与重大灾害国际宪章>旨在促进各空间机构与空间系统运营商之间在利用空间设施进行灾害危机管理方面的合作,在危机期间向遭受自然或技术灾害影响的国家或群体提供数据、信息及服务,支持受灾国家的灾害管理决策,减轻灾害对人类造成的损失.我国在2008年汶川地震后启动合作机制,取得了灾区情况的重要资料,有力地支持了救灾工作.天基信息保障对于救灾具有重大意义,而国际合作机制加强了这种保障.空间减灾宪章实践了外空法之国际合作原则,空间减灾需要进一步加强国际合作.     

响应变量缺失下半参数变系数EV模型的约束统计推断

文章研究了半参数变系数EV模型在线性约束条件下的估计和检验问题,当响应变量缺失、非参数部分协变量带有测量误差时,利用局部纠偏的Profile最小二乘估计、Lagrange乘子方法和借补技术构造了回归模型参数分量两类纠偏约束估计量。此外,为了检验线性约束条件,构造了借补的Profile Lagrange乘子检验统计量,并通过蒙特卡洛数值模拟验证估计量和检验统计量的有效性。     

Simulating Behavioral Influences on Community Flood Risk under Future Climate Scenarios

Flood risk is a function of both climate and human behavior, including individual and societal actions. For this reason, there is a need to incorporate both human and climatic components in models of flood risk. This study simulates behavioral influences on the evolution of community flood risk under different future climate scenarios using an agent-based model (ABM). The objective is to understand better the ways, sometimes unexpected, that human behavior, stochastic floods, and community interventions interact to influence the evolution of flood risk. One historic climate scenario and three future climate scenarios are simulated using a case study location in Fargo, North Dakota. Individual agents can mitigate flood risk via household mitigation or by moving, based on decision rules that consider risk perception and coping perception. The community can mitigate or disseminate information to reduce flood risk. Results show that agent behavior and community action have a significant impact on the evolution of flood risk under different climate scenarios. In all scenarios, individual and community action generally result in a decline in damages over time. In a lower flood risk scenario, the decline is primarily due to agent mitigation, while in a high flood risk scenario, community mitigation and agent relocation are primary drivers of the decline. Adaptive behaviors offset some of the increase in flood risk associated with climate change, and under an extreme climate scenario, our model indicates that many agents relocate.     

ECM模型短期动态系数与协整模型长期均衡系数不一致性研究

研究了ECM模型中短期动态系数和协整模型中长期均衡系数之间的不一致性。根据协整理论,因变量的变化可以分解为基本面的变化和暂时性的噪声成分,短期动态系数由此产生。然而变量的差分减小了基本面的影响,同时提升了噪声对真实经济关系的扭曲。短期动态系数的推导过程进一步表明,噪声是造成短期动态系数小于长期均衡系数的原因。因变量变化越大,噪声的影响越小。国内外铜期货市场整合检验的实证结果与理论分析的预测相一致,实证结果同时显示,误差修正项的引入对短期动态系数的估计没有显著影响;当因变量变化巨大时,短期动态系数接近于长期均衡系数。     

Fitting semiparametric accelerated failure time models for nested case–control data

A nested case–control (NCC) study is an efficient cohort-sampling design in which a subset of controls are sampled from the risk set at each event time. Since covariate measurements are taken only for the sampled subjects, time and efforts of conducting a full scale cohort study can be saved. In this paper, we consider fitting a semiparametric accelerated failure time model to failure time data from a NCC study. We propose to employ an efficient induced smoothing procedure for rank-based estimating method for regression parameters estimation. For variance estimation, we propose to use an efficient resampling method that utilizes the robust sandwich form. We extend our proposed methods to a generalized NCC study that allows a sampling of cases. Finite sample properties of the proposed estimators are investigated via an extensive stimulation study. An application to a tumor study illustrates the utility of the proposed method in routine data analysis.     

Equivalence of two tests in varying coefficient partially linear errors in variable model with missing responses

This paper considers two tests on varying coefficient partially linear errors-in-variables models (VCPLM-EV) with missing responses under the linear constraint. The restricted estimator for the parametric component is derived and proven to share asymptotically normal distribution. In order to test the linear constraint, two statistics based on the profile Lagrange multiplier method and the corrected residual sum of squares method respectively, are proposed. It is of interest to obtain that the magnitudes of the two statistics are equal exactly and follow the asymptotical chi-square distribution. This reveals a new type of Wilk’s phenomenon in VCPLM-EV models with missing response. Finally, some numerical examples are carried out to illustrate relevant performances.     

A Case Study Evaluating the Risk of Infection from Middle Eastern Respiratory Syndrome Coronavirus (MERS‐CoV) in a Hospital Setting Through Bioaerosols

Middle Eastern respiratory syndrome, an emerging viral infection with a global case fatality rate of 35.5%, caused major outbreaks first in 2012 and 2015, though new cases are continuously reported around the world. Transmission is believed to mainly occur in healthcare settings through aerosolized particles. This study uses Quantitative Microbial Risk Assessment to develop a generalizable model that can assist with interpreting reported outbreak data or predict risk of infection with or without the recommended strategies. The exposure scenario includes a single index patient emitting virus‐containing aerosols into the air by coughing, leading to short‐ and long‐range airborne exposures for other patients in the same room, nurses, healthcare workers, and family visitors. Aerosol transport modeling was coupled with Monte Carlo simulation to evaluate the risk of MERS illness for the exposed population. Results from a typical scenario show the daily mean risk of infection to be the highest for the nurses and healthcare workers (8.49 × 10^?4 and 7.91 × 10^?4, respectively), and the lowest for family visitors and patients staying in the same room (3.12 × 10^?4 and 1.29 × 10^?4, respectively). Sensitivity analysis indicates that more than 90% of the uncertainty in the risk characterization is due to the viral concentration in saliva. Assessment of risk interventions showed that respiratory masks were found to have a greater effect in reducing the risks for all the groups evaluated (>90% risk reduction), while increasing the air exchange was effective for the other patients in the same room only (up to 58% risk reduction).     

Influence and residuals in restricte generalized linear models

A particular influence measure for restricted regression models is reviewed in this paper. We give em- phasis on establishing regularity conditions to apply the proposed influence measure in restricted gen- eralized linear models. The development of conditional residuals is also discussed. In particular, a sim- ulation study was conducted in order to compare the distributions of the proposed residuals for various generalized linear models. Finally, an application is given.