Hierarchical Bayesian Modeling of Post-Earthquake Ignition Probabilities Considering Inter-Earthquake Heterogeneity

Post-earthquake fires are high-consequence events with extensive damage potential. They are also low-frequency events, so their nature remains underinvestigated. One difficulty in modeling post-earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter-earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post-earthquake fires. Thus, the present study analyzes the post-earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi-oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source-to-source variability in the data. The heterogeneity of post-earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post-earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post-earthquake ignition probabilities.     

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.     

Uniform consistency rate of kNN regression estimation for functional time series data

In this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried out to illustrate the finite sample performances and the usefulness of the kNN approach.     

Juvenile Delinquency Under Conditions of Rapid Social Change

Increased juvenile delinquency can be seen as an indicator for a deteriorating social fabric under conditions of rapid social change. Criminological theories suggest, however, that such conditions do not per se produce delinquency: They force youth into prodelinquent leisure activities with peers, leading to an endorsement of delinquent behavior and offering the infrastructure for it. Resources acquired in family and public life, however, may prevent youth from drifting into such prodelinquent leisure activities and indirectly from delinquent behavior. Empirical tests of these hypotheses have to employ simultaneous analyses of societal-level and individual-level data. On the basis of an international youth study, the paper undertakes such an analysis: In the years 1992–95, data from 42 independent samples of seventh graders from East and West Germany, Poland, Russia, Bulgaria, Slovakia, Hungary, Czechia, and Greece were surveyed. Data on deviant school behavior, delinquent drift, nurturant parenting, and subjective feelings of justice in public were obtained from 7282 thirteen- to fourteen-year-olds. Processes of rapid social change were incorporated as data on changes in GNP in the years proceeding the surveys. Analyses show that delinquent drift is a valid predictor of deviant school behavior only on the individual level. The extent to which adolescents engage in prodelinquent peer activities depends more on the cultural context in which adolescents live than on their personal experience in the family and in public. Nurturant parenting does, however, covary negatively with deviant school behavior irrespective of level of analysis.     

A class of symmetric bivariate uniform distributions

A class of symmetric bivariate uniform distributions is proposed for use in statistical modeling. The distributions may be constructed to be absolutely continuous with correlations as close to±1 as desired. Expressions for the correlations, regressions and copulas are found. An extension to three dimensions is proposed.     

Linguists and other animals

Book reviewed in this article:
Joan Dunayer, Animal Equality: Language and Liberation     

基于WEB的远程教学管理软件分析与设计

本论文主要讨论了基于WEB远程教学管理软件的分析与设计。首先对远程教育与传统教育进行了比较研究,确定远程教育是现代教育发展的方向;其次对开发基于WEB远程教学管理软件进行了环境分析和需求分析;最后讨论了远程教学管理软件的实现,包括软件模块结构设计和网站的总体框架。     

体裁分析在商务语篇教学中的作用

文章介绍了近年来在美国、澳大利亚等国盛行的“体裁教学法”,评价了体裁和体裁分析理论,阐述了体裁分析的发展与特色,探讨了体裁分析法在商务语篇教学中的作用。     

GIS的路径分析算法及实现

讨论了地理信息系统GIS的路径分析算法 ,并在MAPGIS环境下 ,以西南科技大学道路网为例 ,利用VC 及MAPGIS二次开发类库实现了其最短路径和最佳路径分析。     

绍兴市贷款产出效率分析

通过对1991-2004年绍兴市实现的GDP与占用贷款变动关系的统计分析,指出贷款产出效率持续走低存在的问题,在分析了贷款产出效率下降的主要原因后,提出了相应的对策建议.