空间面板杜宾模型的Bootstrap Wald-COMFAC检验研究

将共同因子约束(COMFAC)的Wald检验问题引入到空间面板模型中,讨论空间面板杜宾模型与空间面板误差模型的识别问题。蒙特卡洛模拟表明:在有限样本下,基于渐近临界值的Wald检验有着良好的检验功效,但存在着较为严重的尺度扭曲。进一步采用残差Bootstrap方法,在不损失检验功效的前提下,能够显著地降低检验的尺度扭曲。因此,残差Bootstrap方法是更为有效的检验方法。     

“婚变”的背后——浅析《墙头马上》中的悲剧因素

一直以来人们都认为《墙头马上》为一部爱情喜剧,通过对此剧中的悲剧性因素悲剧性行动和悲剧性环境的分析,认为此剧并不是仅仅停留在爱情喜剧的层面,而是一部具有社会性婚姻问题的戏剧.     

船舶反弯扭螺旋桨水力特性的数值模拟与分析

运用计算流体力学方法(CFD)对船舶反弯扭螺旋桨的水力特性进行数值模拟计算,获得了螺旋桨的推力系数、扭矩系数及敞水效率等水力特性参数,与试验结果进行了比较,两者吻合良好.模拟计算提供了详细的三维流场分布,可对反弯扭桨叶的工作特性和叶梢涡、尾涡及气蚀的分布状态进行深入的分析.在叶面邻近区域采用层叠式的半结构网格以适应边界层的计算要求,在边界层外的流动区域采用非结构网格,取得了较高的计算精度.     

Multivariate process dispersion monitoring without subgrouping

The memory-type adaptive and non-adaptive control charts are among the best control charts for detecting small-to-moderate changes in the process parameter(s). In this paper, we propose the Crosier CUSUM (CCUSUM), EWMA, adaptive CCUSUM (ACCUSUM) and adaptive EWMA (AEWMA) charts for efficiently monitoring the changes in the covariance matrix of a multivariate normal process without subgrouping. Using extensive Monte Carlo simulations, the length characteristics of these control charts are computed. It turns out that the ACCUSUM and AEWMA charts perform uniformly and substantially better than the CCUSUM and EWMA charts when detecting a range of shift sizes in the covariance matrix. Moreover, the AEWMA chart outperforms the ACCUSUM chart. A real dataset is used to explain the implementation of the proposed control charts.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Conditional moving linear regression: Modeling the recruitment process for ALLHAT

Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N = 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials.     

‘Media Events’ reconsidered: from ritual theory to simulation and performativity

This paper re-examines the long-established notion of ‘media events’ by contrasting and critically appraising three distinct approaches to the question of media events. These are: ritual theory associated with Daniel Dayan and Elihu Katz, secondly, Jean Baudrillard’s approach rooted in his notions of simulation and ‘non-events’ and, finally, the more recent performative approaches to media and mediation. I take Sarah Kember and Joanna Zylinska’s reading of media events presented in Life After New Media (2012) as exemplary of the performative approach. An argument is made that the accounts of media events offered by performative approaches add very little, and, indeed, lack the critical insightfulness of the earlier approaches. Both ritual theory and Baudrillard’s thought are briefly reappraised and, against Nick Couldry, I try to show that these accounts are not characterised by binary and reductive thinking. The major misunderstandings concern the nature of the sacred and profane dualism and the further dualisms developed in Baudrillard’s thought, particularly the figures of implosion and reversibility. Finally, Baudrillard’s position on technology is addressed and the paper concludes with the suggestion that his account is not solely negative, since technological developments are not only at the mercy of ironic reversals they may also enable new rituals of disappearance.     

Bayes Inference in Constant Partially Accelerated Life Tests for the Generalized Exponential Distribution with Progressive Censoring

In this paper, the problem of constant partially accelerated life tests when the lifetime follows the generalized exponential distribution is considered. Based on progressive type-II censoring scheme, the maximum likelihood and Bayes methods of estimation are used for estimating the distribution parameters and acceleration factor. A Monte Carlo simulation study is carried out to examine the performance of the obtained estimates.     

Updating discriminant functions estimated from inverse gaussian populations

The problem of updating discriminant functions estimated from inverse Gaussian populations is investigated in situations when the additional observations are mixed (unclassified) or classified. In each case two types of discriminant functions, linear and quadratic, are considered. Using simulation experiments the performance of the updating procedures is evaluated by means of relative efficiencies.     

Estimation of odds ratios under order restrictions

A new method for estimating a set of odds ratios under an order restriction based on estimating equations is proposed. The method is applied to those of the conditional maximum likelihood estimators and the Mantel-Haenszel estimators. The estimators derived from the conditional likelihood estimating equations are shown to maximize the conditional likelihoods. It is also seen that the restricted estimators converge almost surely to the respective odds ratios when the respective sample sizes become large regularly. The restricted estimators are compared with the unrestricted maximum likelihood estimators by a Monte Carlo simulation. The simulation studies show that the restricted estimates improve the mean squared errors remarkably, while the Mantel-Haenszel type estimates are competitive with the conditional maximum likelihood estimates, being slightly worse.