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.     

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.     

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.     

大学英语作文非语法错误分析及纠错策略

通过深入分析非英语专业大学生英语作文中出现的内容、篇章结构、语言表达等方面的非语法错误,提出了写前阶段构思、审题技巧训练及寓写作于精读课教学等相应的纠错策略。     

开放式数控切割机神经网络误差补偿研究

分析了影响数控火焰切割机加工精度的主要因素,利用开放式数控系统的软件开放性,提出了采用IGCAQBP学习算法的神经网络方法来对包括金属热变形、机械传动误差等非线性因素在内的多种因素造成的加工误差进行误差补偿,设计了嵌入开放式数控系统中的神经网络误差补偿器,给出了实用的补偿器使用方法,并对误差补偿功能进行了扩展,仿真结果和实际应用表明该方法稳定有效。     

英语写作中有效反馈信息的探讨

文章简要阐述了反馈信息,论述了作文工作研究的意义及在作文写作中有效的信息反馈形式,提出了增强写作能力的可行措施。     

关于独立董事制度的一个经济逻辑分析

运用体制转轨和渐进改革的宏观视角、使用制度演进和制度创新的分析方法、结合公司治理结构的发展,对独立董事制度的构建背景、意义、发展途径进行一个比较详尽深入的经济逻辑分析,认为在制度潜力发掘—市场规范发展彼此良性互动的作用机制下,独立董事制度将逐渐成熟壮大,最终形成对原有公司治理机构体系的冲击,促使公司治理结构的不断优化和市场的不断成熟规范化变迁。     

串联稳压器基准误差放大电路的设计

针对串联型稳压器,设计了一种应用于串联型稳压器具有自建基准的新型误差放大电路。该电路具有构思巧妙,结构优化,易于集成及较高的开环增益,共模抑制比及交流特性的优点。通过验证,实测数据与仿真结果基本一致。     

调节教学评价的研究

调节教学评价是以推进素质教育为宗旨 ,以“反馈矫正”为基本手段 ,以评价主体间的双向动态调节为主要特征 ,促进教师和学生共同发展的过程。调节教学评价的具体实施程序为 :发展定向—评价反馈—调节矫正。具体的实施要求是 :确立多元化评价指标 ,注重评价的反馈调节功能 ,质的评价和量化评价相结合 ,形成性评价与终结性评价相结合 ,他评与自评相结合     

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables, Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.