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.     

英文影片的中国方言版字幕翻译

2012年3D 版《泰坦尼克号》的华丽上映曾掀起影迷们对它的新一轮热捧。文章就同样备受中国观众喜爱的中国方言版《泰坦尼克号》为案例，结合功能目的论来探寻中国方言版字幕翻译的目的与性质，进而分析、总结英文影片字幕的中国方言版译制活动过程。     

Using local linear kernel smoothers to test the lack of fit of nonlinear regression models

Herein, we propose a data-driven test that assesses the lack of fit of nonlinear regression models. The comparison of local linear kernel and parametric fits is the basis of this test, and specific boundary-corrected kernels are not needed at the boundary when local linear fitting is used. Under the parametric null model, the asymptotically optimal bandwidth can be used for bandwidth selection. This selection method leads to the data-driven test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The finite-sample property of the proposed data-driven test is illustrated, and the power of the test is compared with that of some existing tests via simulation studies. We illustrate the practicality of the proposed test by using two data sets.     

基于定货合同的供应商柔性分析和选择模型

供应链及供应链管理是目前国内外学术界和工商企业管理者共同关心的课题,他们提出了众多分析方法和分析模型,但由于计算复杂,在实际操作中运用较少。实际上,在供应链业务流程中,供应合同是最关键的法律文件,合同信息分析能很好地减少不确定性,降低风险。因此,供应商和购货商选择模型应包括以下参数:单位供应价格、定货周期、最小定货提前期、每个周期最小定货量、临时配送补偿系数、定货量小于最小定货量补偿系数。     

论邓小平对选拔培养中青年干部的思想

建设有中国特色的社会主义市场经济 ,需要一大批杰出的人才。如何选拔培养中青年干部成为我们党和国家一项重大课题。运用邓小平理论中关于选拔培养中青年干部的观点并指导实践 ,将会对我国的四化建设起到积极作用     

话语前提特定意义的选择

在特定语境中 ,话语的含义只有一个 ,却可能有多个前提 ,一个前提也可能有多重意义。那么 ,在会话过程中 ,说话者是如何设定话语前提的 ,听话者又是如何对话语前提进行选择和理解的 ?本文从分析前提感知的失误入手 ,运用逻辑分析方法 ,从认知语用学角度考察听话者对预设前提的感知 ,分析选择前提特定意义的原则、策略和过程 ,进而提出前提空间定位的心理模式。笔者认为前提的选择有逻辑 -语义分析、语用推理和整合等三种不同等级的策略 ;话语前提的选择是感知 -推理 -理解 -选择的探索性过程。由此得出结论 :认知环境是话语前提的决定性因素 ,话语前提的正确选择可再现话语的语境表征 ,为话语内涵的理解提供信息表征和推理依据———基于真值条件的最佳关联     

转型时期我国农民的理智抉择

社会转型,农民主体意识觉醒,价值取向市场化、现代化。非农就业成为农民的理智抉择。抉择具有合理性和必然性,既符合我国国情,又符合国家的发展战略,是现代化发展的必由之路。     

Assessing goodness-of-fit of categorical regression models based on case-control data

Demonstrated equivalence between a categorical regression model based on case‐control data and an I‐sample semiparametric selection bias model leads to a new goodness‐of‐fit test. The proposed test statistic is an extension of an existing Kolmogorov–Smirnov‐type statistic and is the weighted average of the absolute differences between two estimated distribution functions in each response category. The paper establishes an optimal property for the maximum semiparametric likelihood estimator of the parameters in the I‐sample semiparametric selection bias model. It also presents a bootstrap procedure, some simulation results and an analysis of two real datasets.     

Adaptive tests of regression functions via multiscale generalized likelihood ratios

Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. She derives the asymptotic joint distribution of the component tests and that of the proposed test under the null hypothesis. She also develops a simple method of selecting the smoothing parameters for the proposed test and presents two approximate methods for obtaining its P‐value. Finally, she evaluates the proposed test through simulations and illustrates its application to a set of real data.     

Herd behavior and the quality of opinions

This paper analyzes a decentralized decision model by adding some inertia in the social leaning process. Before making a decision, an agent can observe the group opinion in a society. Social learning can result in a variety of equilibrium behavioral patterns. For insufficient ranges of quality (precision) of opinions, the chosen stationary state is unique and globally accessible, in which all agents adopt the superior action. Sufficient quality of opinions gives rise to multiple stationary states. One of them will be characterized by inefficient herding. The confidence in the majority opinion then has serious welfare consequences.