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.     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

宋代閤职的内外演变与磨勘叙迁探析

宋代共设有三等閤职，最初供职于閤门，后閤职官员出外任职情况变得普遍，直接推动了外任武官兼带閤职制度的发展，此后閤职作为武官兼带的一种职名，成为奖励军功以及控制武官的一种有效手段。而閤职制度的发展也使閤职作为武臣清选的地位逐渐确立，表现之一就是在磨勘叙迁上较一般武官而言具有一定的优越性。同时，内外不同的閤职在磨勘制度上也有一定的差异，这些差异的产生也加深了閤职作为武臣清选的内涵。     

企业资源配置战略与盈余持续性

盈余持续性是盈余质量的重要特征。较高的盈余持续性预示着企业的盈余质量较高,在一定程度上表明企业的经营状况平稳并具有良好的管理水平,有利于企业降低经营风险、提升公司价值。近年来,盈余持续性已经成为资本市场研究领域的一个热点问题。以我国A股市场2007—2017年非金融类上市公司为样本,对企业资源配置战略与盈余持续性之间的关系进行了实证研究。研究结果表明,经营主导型公司的盈余持续性要显著强于投资主导型公司;进一步区分具体的盈余构成后发现,经营主导型公司核心利润的持续性显著强于投资主导型公司,但其投资收益的持续性显著弱于投资主导型公司。上述结论表明,盈余及其构成的持续性均会受到公司资源配置战略的影响。相关研究结论既为公司资源配置战略影响盈余特征提供了经验证据,又对外部利益相关者透过战略本质看清会计信息的内在价值具有一定的启示作用。     

地方党政主要领导职位空缺的现状、成因及对策

地方党政主要领导职位空缺是指在领导干部选任和调配过程中，由于诸多常规或非常规原因导致某一地区的党委或政府部门主要领导职位在较长时间内空缺，进而直接或间接影响地方党政工作的特殊现状，广泛存在于各级、各地党政部门。由于我国有关干部职位空缺的制度建设和管理规范尚不完善，地方党政主要领导职位空缺产生一些消极影响，主要表现为不利于地方党政机关运行，存在腐败风险，触发舆论风险等。在充分认识空缺危害性的基础上，深入分析造成职位空缺的干部调配因素、选任导向变革因素以及其他非常规因素等，从完善遴选机制、衔接制度、保障机制、舆论生态治理、干部教育等五个方面切实解决地方党政主要领导职位空缺带来的一系列问题。     

Variable selection and estimation for high-dimensional spatial autoregressive models

Spatial regression models are important tools for many scientific disciplines including economics, business, and social science. In this article, we investigate postmodel selection estimators that apply least squares estimation to the model selected by penalized estimation in high-dimensional regression models with spatial autoregressive errors. We show that by separating the model selection and estimation process, the postmodel selection estimator performs at least as well as the simultaneous variable selection and estimation method in terms of the rate of convergence. Moreover, under perfect model selection, the ℓ₂ rate of convergence is the oracle rate of $\sqrt{s/n}$, compared with the convergence rate of $\text{◂√▸}\sqrt{s\log p/n}$ in the general case. Here, n is the sample size and p, s are the model dimension and number of significant covariates, respectively. We further provide the convergence rate of the estimation error in the form of $\sup$ norm, and ideally the rate can reach as fast as $\text{◂√▸}\sqrt{\log s/n}$.     

Family Characteristics and Mate Selection: Evidence From Computer‐Assisted Dating in Japan

Research on mate selection rarely considers singles' preferences for their future partners' family configurations and experiences. Using online dating records from a matchmaking agency in Japan, a society with a strong emphasis on family and kinship, we examine how singles' responses to date requests correspond to potential mates' family circumstances. Results showed that singles' preferences for potential partners' family characteristics stem from a concern about future obligations toward the partner's family and stereotypes associated with certain family traits. Singles are less likely to accept requests from those from large families, which are seen as traditional. Being from a large family, however, hampers individuals' dating chances more if they are firstborn and have no brothers, two conditions that make them the designated child to care for elderly parents. We also find that Japanese singles seek partners with more of the universally valued family traits rather than traits similar to their own.     

Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.