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.     

Interval estimation for conformance proportions of multiple quality characteristics

A conformance proportion is an important and useful index to assess industrial quality improvement. Statistical confidence limits for a conformance proportion are usually required not only to perform statistical significance tests, but also to provide useful information for determining practical significance. In this article, we propose approaches for constructing statistical confidence limits for a conformance proportion of multiple quality characteristics. Under the assumption that the variables of interest are distributed with a multivariate normal distribution, we develop an approach based on the concept of a fiducial generalized pivotal quantity (FGPQ). Without any distribution assumption on the variables, we apply some confidence interval construction methods for the conformance proportion by treating it as the probability of a success in a binomial distribution. The performance of the proposed methods is evaluated through detailed simulation studies. The results reveal that the simulated coverage probability (cp) for the FGPQ-based method is generally larger than the claimed value. On the other hand, one of the binomial distribution-based methods, that is, the standard method suggested in classical textbooks, appears to have smaller simulated cps than the nominal level. Two alternatives to the standard method are found to maintain their simulated cps sufficiently close to the claimed level, and hence their performances are judged to be satisfactory. In addition, three examples are given to illustrate the application of the proposed methods.     

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。     

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。     

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.     

构建社会主义和谐社会的哲学思考

构建社会主义和谐社会的战略决策是我们党对新世纪、新阶段我国经济社会发展的新要求和我国社会出现的新趋势、新特点的科学判断和理性抉择。要从社会有机体的理论视角阐释物质文明、政治文明、精神文明和和谐社会的"四位一体"建设;要不断认识和把握和谐社会建设的特点和规律;要以马克思主义关于社会主义社会建设理论为科学的理论指导,深入调查研究,不断提高构建社会主义和谐社会的能力。     

英语写作教学中语法反馈的探讨

语法问题一直是外语和第二语言教学广泛关注且颇有争议的话题。论述了英语写作教学中反馈及其作用,以及有关语法改错的讨论,并提出了有效反馈的具体方法及语法在写作教学中应有的位置。