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.     

Multiple event times in the presence of informative censoring: modeling and analysis by copulas

Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.

     

理智与情感:为历史“正名”的方法——从刘保昌长篇小说《楚武王》谈起

历史书写中的“最炫流行风”在今天的文化市场上大行其道——用拆解、谐谑、碎片化、戏拟等方式拉平历史的深度,以“傲慢与偏见”来充抵叙述历史的自信,也经由这种时尚化的叙述使历史以“轻松一刻”的形式实现消费欲望的满足.刘保昌的《楚武王》则疏离“流行前线”,以史料为据,正本清源,守正而出新,为历史“正名”,以彰显历史意识、历史理性的写作姿态,以坚持文学之理想力量的叙事激情,以有文化承担的叙事态度显示出对当代历史小说叙事品格与尊严的坚守.     

A modified common factor model for modelling mortality jointly for both sexes

Journal of Population Research - There is an increasing attention on the joint modelling of multiple populations. Populations are related in several ways, such as neighbouring countries, females...     

Has Opposition to Immigration Increased in the United States after the Economic Crisis? An Experimental Approach

We employ two population‐level experiments to accurately measure opposition to immigration before and after the economic crisis of 2008. Our design explicitly addresses social desirability bias, which is the tendency to give responses that are seen favorably by others and can lead to substantial underreporting of opposition to immigration. We find that overt opposition to immigration, expressed as support for a closed border, increases slightly after the crisis. However, once we account for social desirability bias, no significant increase remains. We conclude that the observed increase in anti‐immigration sentiment in the post‐crisis United States is attributable to greater expression of opposition rather than any underlying change in attitudes.     

1950年代:“本质—特征论”文学定义的形成及影响

1950年代形成了"本质—特征论"的文学定义,即先界定文学的意识形态本质,再界定文学是用形象的方式来反映社会生活、体现意识形态的特征。这一定义的思维方式是先界定事物的普遍性,再界定事物的特殊性,突出事物的普遍性关联,轻视事物的独特性质的揭示,使得文学研究本应着重研究文学的特殊性成为空话,不利于认识文学自身的规律。1980年代的文学审美论试图突破这种定义方式,但1990年代以来,反本质主义的文学语境论再次回到从事物的关联性定义事物的思维老路,文学自身本质问题受到质疑,弱化了文学与审美关联的重要性与必要性。     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

Partial Identification of Economic Mobility: With an Application to the United States

Abstract

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this article, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession.     

高含水期油藏层间压力结构差异量化方法研究

由于长期注水开发和层间非均质性影响，高含水期油藏各小层层间压力结构差异严重，掌握差异状况是合理调整的重要依据。针对现场分层测压资料少，单层压力资料获取困难的情况，应用渗流力学原理建立了高含水期地层层间压力结构差异量化数学模型，模型的关键参数可以通过生产数据计算获得。枣园油田的实例验证表明，高含水油藏层间压力存在明显差异，该方法能够确定各小层的压力差异状况。量化计算结果和实测资料相接近，有利于认清开发矛盾，为合理调整提供技术依据。