Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Ethnicity, migration and health: A population-based study of 338 refugees from Latin America, 396 nonrefugee immigrants from Finland and 161 from southern Europe and 996 age-, sex- and education-matched controls from Sweden

This article shows the influence of being a refugee from Latin America or a nonrefugee immigrant from southern Europe or Finland on self-reported illness, controlling for social factors and lifestyle. The study population consisted of 338 Latin American refugees, a random sample of 396 Finnish and 161 southern European immigrants and 996 age-, sex- and education-matched Swedish controls. The data were analysed unmatched with logistic regression (multivariate analysis) in main effect models. The strongest independent risk indicator for long-term illness was being a Latin American refugee (estimated odds ratio (OR)=2.96, 95% confidence interval (CI)=2.19–3.82). There was a significant association between being a Latin American refugee and period prevalence, ill health and unsatisfied need for care. Being a southern European or Finnish immigrant was a risk indicator of ill health but was not associated with the other dependent factors. Not feeling secure in daily life was a strong risk indicator for long-term illness and ill health (estimated OR=1.89, 95% CI=1.26–2.76 and OR=3.04, 95% CI= 1.97–4.48) respectively). Being a Latin American refugee was equal in importance to traditional risk factors such as overweight and not taking regular exercise for long-term illness and ill health.     

论我国农村基层民主建设的现实基础

基层民主建设是有中国特色的民主政治内容之一。近年来,我国农村的基层民主建设引起世人关注。本文从我国农村的经济因素、社会因素和文化因素三个方面探讨了在经济、文化相对落后的农村推进民主进程的可能性与可行性。     

几类全微分方程问题的求解定理

利用全微分方程的条件，给出一类微分方程的积分因子及通解公式，得出一类全微分方程中未知函数所满足的二阶线性微分方程，获得未知函数及全微分方程的通解。     

开发我国人力资源创新能力的对策研究

创新是推动社会发展的动力 ,特别是在知识经济时代 ,一个国家竞争能力的强弱主要依赖于创新 ,而创新源于具有创新能力的人才。笔者从分析影响人力资源创新能力的心理因素和社会环境因素入手 ,针对这些影响因素提出了开发我国人力资源创新能力的对策。     

Estimation of location extremes within general families of scale mixtures

In this paper, we study the estimation of the minimum and maximum location parameters, respectively, representing the minimum guaranteed lifetime of series and parallel systems of components, within a general class of scale mixtures. The conditional or underlying distribution has only the primary restriction of being a location-scale family with positive support. The mixing distribution is also quite general in that we only assume that it has positive support and finite second moment. For demonstrative purposes several special cases are highlighted such as the gamma, inverse-Gaussian, and discrete mixture. Various estimators, including bootstrap bias corrected estimators, are compared with respect to both mean-squared-error and Pitman's measure of closeness.     

顺应历史发展潮流,重塑多元价值理论--论深化改革与促进发展的经济学价值论基础

具有历史局限性的劳动价值论受到实践挑战并被其他理论超越是不可避免的.社会经济发展的条件、动力和主体多元化了,与其相适应的经济价值论也必然要多元化.商品价值的本质是人的需求同商品之间的关系.商品价值的决定是一个多层次多元的综合体系,它由"天人合一"论、供求均衡论、生产要素论和需求要素论所构成,劳动价值论只是其中的"特例".生产要素贡献论与合法产权收益论是市场经济条件下必然的收益分配原则,它对"一家独享"和"吃大锅饭"的不平等都是排斥的.劳动价值论所认定的剥削只是一种极端情形,而依据多元价值论则能对剥削作出更具普遍性的说明.资本的性质及作用取决于它的来源和使用方式.中国现阶段的大多数民营企业家为国为民做出了重大贡献,不应一概斥之为剥削阶级."全心全意依靠工人阶级"的实质是指始终要依靠先进生产力的代表,而不是固定不变的哪部分人群,它同"科学技术是第一生产力"应当是统一的.随着生产发展和社会进步,中产阶级壮大不可避免,但这不是坏事,毕竟我们革命和建设不是要制造更多无产者.中产阶级在社会阶级或阶层构成中占相当大的一个多数是实现社会稳定和发展的重要条件.     

受内压圆柱筒体开孔-接管区应力集中的有限元分析

利用 ANSYS有限元分析软件通过三个算例对圆柱形筒体小开孔直接管区、大开孔直接管区、大开孔斜接管区的应力分布状况进行了研究 .从中了解到了最大第一主应力的变化规律 ,得出在弹性范围内的应力集中系数 ,并且得到ASME规范中的算式较好的验证     

Empirical likelihood confidence intervals for the mean of a population containing many zero values

If a population contains many zero values and the sample size is not very large, the traditional normal approximation‐based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. In the context of survey sampling, however, there is a general preference for making minimal assumptions about the population under study. The authors have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. They show that under a variety of hypothetical populations, these intervals often outperformed parametric likelihood intervals by having more balanced coverage rates and larger lower bounds. The authors illustrate their methodology using data from the Canadian Labour Force Survey for the year 2000.     

Bayesian analysis of mixture modelling using the multivariate t distribution

A finite mixture model using the multivariate t distribution has been shown as a robust extension of normal mixtures. In this paper, we present a Bayesian approach for inference about parameters of t-mixture models. The specifications of prior distributions are weakly informative to avoid causing nonintegrable posterior distributions. We present two efficient EM-type algorithms for computing the joint posterior mode with the observed data and an incomplete future vector as the sample. Markov chain Monte Carlo sampling schemes are also developed to obtain the target posterior distribution of parameters. The advantages of Bayesian approach over the maximum likelihood method are demonstrated via a set of real data.