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。     

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.     

高频数据瞬时波动率核估计的窗宽选择及算法研究

在瞬时波动率的各种估计量中，非参数估计量因其能准确地度量瞬时波动率，一直是学者们的研究热点。然而，这类估计量在实际应用中都面临着最优窗宽的确定问题。由于最优窗宽中往往携带一些难以估计的未知参数，使得在实际应用过程中确定最优窗宽的具体数值存在困难。本文以瞬时波动率的核估计量为例，借鉴非参数回归分析中窗宽选择的思想，构建了一种能从数据中准确计算出最优窗宽具体值的算法。理论的分析和数值上的验证表明：文中所构建的算法具有良好的稳定性、适应性和收敛速度。算法的提出为瞬时波动率的后续应用研究铺平道路。     

异质品双渠道供应链下制造商的渠道决策研究

研究由两个制造商、两个零售商以及消费者组成的双渠道供应链，制造商的最优产品分销渠道策略问题，通过构建Stackelberg主从动态博弈模型，分析制造商在不同渠道选择下的产品质量水平及定价决策，消费者三种不同渠道偏好下渠道竞争和品牌竞争激烈程度对制造商渠道选择均衡结果及帕累托最优选择的影响，以及对产品质量水平、产品价格、制造商及零售商利润的影响.研究表明：品牌和渠道双重竞争下，两种竞争之间存在相互作用的关系，并共同影响制造商最终渠道选择，制造商通过权衡价格决策的后动优势与消费者渠道偏好对其利润的贡献选择是否开辟双渠道，产品价格与产品质量水平正相关，与产品的替代率负相关，产品质量水平对渠道价格的影响程度等于消费者对该渠道的偏好程度.当消费者偏好某一渠道时，制造商可在该渠道上的产品采取优质高价策略，但渠道上产品性价比降低，当品牌竞争越激烈，产品价格越低，对制造商和零售商的利润挤压越严重.     

基于股价预测的泛证券投资组合策略

本文基于期望效用最大化和L₁-中位数估计研究了在线投资组合选择问题。与EG（Exponential Gradient）策略仅利用单期价格信息估计价格趋势不同，本文将利用多期价格信息估计价格趋势，以提高在线策略的性能。首先，基于多期价格数据，利用L₁-中位数估计得到预期价格趋势。然后，通过期望效用最大化，提出一个新的具有线型时间复杂度的在线策略，EGLM（Exponential Gradient via L₁-Median）。并通过相对熵函数定义资产权重向量的距离，进而证明了EGLM策略具有泛证券投资组合性质。最后，利用国内外6个证券市场的历史数据进行实证分析，结果表明相较于UP（Universal Portfolio）策略和EG策略，EGLM策略有更好的竞争性能。