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.     

Vaginal births after caesarean: What does Google think about it?

AimVaginal birth after caesarean (VBAC) is a relatively safe alternative to repeat caesarean birth in the proper context. This important decision to undergo an elective caesarean versus VBAC is ultimately a decision of the mother. The purpose of our study was to assess the quality of online information in relation to VBAC collected using the most common search engine: Google.MethodsThe 10 most common hit sites for the keywords “VBAC” and “Vaginal birth after caesarean” were evaluated using the search engine Google. The quality of websites was rated based on the Silberg scale for accountability, the modified Abbott's criteria for presentation and the SMOG index for readability. The content of each website was compared to the Society of Obstetricians and Gynecologists of Canada (SOGC) guidelines for VBAC.Findings13 out of 20 identified websites met the adequate criteria for accountability, with 85% of the websites indicating authorship. 11 websites were deemed aesthetically agreeable. The target audience, assessed by the readability score, was notably above the non-medical population with an average SMOG index score of 14.75. Only half of the websites contained recommendations, as detailed by the SOGC guidelines.ConclusionAlmost all sites target a higher academic level, making it beyond the comprehension of the general population. Woman friendly web-assessment tools should be provided to enable pregnant women to take an active role in their decision making.     

The value of being precise

This study investigates how individuals assess imprecise information. We focus on two essential dimensions of decision under uncertainty, outcomes and probabilities, and their respective precision. We believe the precision of information is highly relevant in the investment setting, as reflected in the well-known “home (familiarity) bias”, and the outcome and probability dimensions, separately or jointly, may affect investors’ knowledge of uncertainty and perceived risk of the investment options, and subsequently affect investors’ choices. To test this conjecture, we conducted three experiments. Our results show that 1) participants demonstrate a pattern of preference for precision and aversion of extreme vagueness and associate vagueness with higher perceived risk and lower investment (experiments one and two); 2) participants prefer vague outcome information to vague probability information (experiment two); 3) familiarity indeed positively affects the precision of estimated values, but this association is stronger for the outcome dimension than for probabilities (experiment three). Our results confirm that precision in information, especially in the outcome dimension has an impact on investors’ resource allocation choices.     

互联网深化、信息不确定性与企业出口平稳性

互联网作为信息搜寻和传播的重要渠道,有利于缓解国际市场不确定性对企业出口决策的影响,从而有助于稳定企业出口预期,保障出口企业的持续生存。本文首先基于Fernandes 和 Tang(2014)的研究框架将互联网深化程度和出口集聚同时纳入到异质性贸易企业模型,在剥离出口集聚对企业出口行为影响的基础上,从理论视角揭示了互联网深化程度提高影响企业出口行为特别是出口持续生存的内在机理与方向。研究发现,不同于出口集聚会同时影响企业的需求冲击和成本冲击,互联网深化带来的信息溢出效应会通过贝叶斯法则(Bayesian Rule)使得企业修正其对国际市场不确定性的预期,提高其对目的国市场成本冲击的估计精度,从而在互联网深化程度较高的城市,企业进入出口市场的临界生产率较低,有利于其出口参与度和出口稳定性的提高。然而互联网深化程度提高不影响企业在目的国市场产品出口的集约边际。本文使用2000-2013 年中国工业企业数据库、中国海关数据库和中国城市统计年鉴数据库的匹配数据从企业-目的国-产品层面实证检验了本文的理论命题。研究表明,我国政府应充分发挥互联网发展降低信息不确定性的微观机制,以推动外贸的平稳增长。     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

Visualization of generalized mean estimators using auxiliary information in survey sampling

Abstract

The mean estimators with ratio depend on multiple auxiliary variables and unknown parameters in a finite population setting. We propose a new generalized approach with matrices for modeling the mutivariate mean estimators with two auxiliary variables. Our approach brings naturally a graphical analysis for comparing mean estimators.     

市场分割、政企关系与异地市场拓展

中国的市场仍旧存在某种程度的分割，如何突破这种市场分割到异地拓展市场是企业要考虑的现实问题。以前的研究主要是从省级层面或国家层面进行宏观分析。文章创新地从政企关系视角入手来考察市场分割情形下微观层面的企业异地市场拓展，也是首先把市场分割（和异地市场）分为省内（本市外）市场、省外市场和海外市场三个不同层面来研究的论文。用世界银行的调查数据从微观企业个体层面检验了企业异地市场拓展的可能。实证研究结果发现，市场分割确实阻碍了企业异地市场拓展，但这仅限于省内市场而不是省外市场。此外，市场分割是有方向性的，从地方保护主义严重的市场里走出来的企业更容易拓展异地市场，但此结论仅适用于国内市场拓展而不适用于海外市场拓展。良好的政企关系是有利于企业异地市场拓展的，但仅限于省外市场，对省内市场以及海外市场是无效的。文章解释了其中的原因，并分析了对于企业实践的启示。     

The Myth of the Computer Hacker

The seriousness of computer hacking is not exaggerated, it is far worse than that. The computer hacker has attained the status of myth; society associates all computer crime with a mythical perpetrator that bears no resemblance to reality. This paper will argue that in the early stages of the myth the computer hacker was regarded as a highly skilled but mentally disturbed youth who has an unhealthy association with computers. The new reality of electronic commerce resulted in pressures that culminated in the computer hacker becoming regarded as a dangerous criminal. A thorough analysis of the statistics will demonstrate that the majority of computer intruders are neither dangerous nor highly skilled, and thus nothing like the mythical hacker.     

虚拟空间交织的取向原则

虚拟空间交织是人们常用的思维与认知方式之一。本文通过对交织及其原则的介绍,描述了虚拟空间交织的运作,指出虚拟空间交织构建中主观取向的原则应为最佳关联。